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@ -13,7 +13,7 @@
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"id": "0b365a02",
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"metadata": {},
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"source": [
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"## 先说结论，该数据集（fetch_covtype）Stacking的方法比线性加权更好\n",
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"## 先说结论，该数据集（fetch_covtype）Stacking的方法相比Blending和线性加权更好\n",
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"比赛中我们常用线性加权作为最终的融合方式，我们同样也会好奇怎样的线性加权权重更好，下面也会举例子\n",
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"参考：https://github.com/rushter/heamy/tree/master/examples"
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]
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@ -67,7 +67,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": 1,
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"id": "69632c6a",
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"metadata": {},
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"outputs": [
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@ -86,7 +86,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 61,
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"execution_count": 2,
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"id": "ca421279",
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"metadata": {},
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"outputs": [],
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@ -119,7 +119,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"execution_count": 3,
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"id": "9a0fabe1",
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"metadata": {},
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"outputs": [],
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@ -130,7 +130,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 47,
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"execution_count": 6,
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"id": "5bd75178",
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"metadata": {},
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"outputs": [
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@ -153,14 +153,14 @@
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"print(y)\n",
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"ord = OrdinalEncoder()\n",
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"y = ord.fit_transform(y.reshape(-1, 1))\n",
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"y = y_enc.reshape(-1, )\n",
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"y = y.reshape(-1, )\n",
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"print('七分类任务，处理后：',np.unique(y))\n",
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"print(y)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 48,
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"execution_count": 7,
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"id": "23d9778c",
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"metadata": {},
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"outputs": [
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@ -182,7 +182,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 49,
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"execution_count": 8,
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"id": "eac48668",
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"metadata": {},
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"outputs": [
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@ -204,7 +204,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 50,
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"execution_count": 9,
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"id": "fba3f975",
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"metadata": {},
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"outputs": [
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@ -226,7 +226,7 @@
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" 0.000e+00]])"
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]
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},
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"execution_count": 50,
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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@ -246,7 +246,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"execution_count": 10,
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"id": "e8393e73",
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"metadata": {},
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"outputs": [],
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@ -293,7 +293,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 52,
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"execution_count": 11,
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"id": "78ab0083",
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"metadata": {},
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"outputs": [],
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@ -313,7 +313,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 53,
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"execution_count": 13,
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"id": "173ef0f0",
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"metadata": {},
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"outputs": [
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@ -321,185 +321,485 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Best Score (log_loss): 0.18744137777851164\n",
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"Best Weights: [0.36556831 0.00303401 0.63139768]\n"
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"Best Score (log_loss): 0.18646435443714865\n",
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"Best Weights: [2.53464919e-01 1.48562205e-20 7.46535081e-01]\n",
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"Wall time: 14min 19s\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"array([0.36556831, 0.00303401, 0.63139768])"
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"array([2.53464919e-01, 1.48562205e-20, 7.46535081e-01])"
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]
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},
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"execution_count": 53,
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"pipeline.find_weights(scorer=log_loss, ) # 输出最优权重组合"
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"%%time\n",
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"pipeline.find_weights(scorer=log_loss) # 输出最优权重组合"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 55,
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"execution_count": 22,
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"id": "80726d19",
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Wall time: 1h 39min 20s\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"# 5折训练构建5折模型特征集，这里比较耗时\n",
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"stack_ds = pipeline.stack(k=5,stratify=False,seed=42,full_test=False) # full_test指明预测全部还是预测当前折的验证集"
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"stack_ds = pipeline.stack(k=5,seed=42)"
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]
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},
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"execution_count": 56,
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"execution_count": 26,
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"id": "b25bba3c",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" xgb_0 xgb_1 xgb_2 xgb_3 xgb_4 \\\n",
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"0 0.177179 0.818728 2.185222e-07 9.264143e-09 4.090067e-03 \n",
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"1 0.005155 0.994845 7.055579e-10 1.326343e-08 6.331572e-09 \n",
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"2 0.293492 0.706508 3.650662e-10 1.017633e-09 8.823530e-09 \n",
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"3 0.478112 0.521816 3.207779e-06 2.878019e-08 1.076500e-08 \n",
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"4 0.992430 0.006652 1.233117e-05 1.887496e-07 1.569583e-06 \n",
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"... ... ... ... ... ... \n",
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"435754 0.988518 0.011477 3.190797e-09 5.645121e-08 2.940739e-09 \n",
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"435755 0.969212 0.030723 2.142020e-08 1.572054e-05 4.321913e-07 \n",
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"435756 0.415850 0.584142 4.283793e-08 7.367601e-08 6.148067e-07 \n",
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"435757 0.602601 0.397399 6.606462e-10 1.015894e-09 7.221973e-08 \n",
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"435758 0.834587 0.165411 3.267833e-09 2.057172e-08 2.078704e-08 \n",
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"\n",
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" xgb_5 xgb_6 lgb_0 lgb_1 lgb_2 ... \\\n",
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"0 1.725062e-06 1.048052e-06 0.172406 0.812678 1.416886e-06 ... \n",
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"1 1.435787e-09 1.603579e-10 0.008114 0.991886 0.000000e+00 ... \n",
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"2 6.384080e-10 2.823794e-08 0.817627 0.182372 0.000000e+00 ... \n",
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"3 2.230641e-06 6.630235e-05 0.465733 0.534184 0.000000e+00 ... \n",
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"4 5.604260e-07 9.037877e-04 0.932050 0.043451 0.000000e+00 ... \n",
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"... ... ... ... ... ... ... \n",
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"435754 1.530261e-08 4.466830e-06 0.970593 0.029399 0.000000e+00 ... \n",
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"435755 5.574208e-10 4.977021e-05 0.862591 0.136644 0.000000e+00 ... \n",
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"435756 2.371389e-06 5.185283e-06 0.466886 0.533039 0.000000e+00 ... \n",
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"435757 2.326313e-09 9.193871e-09 0.674250 0.325750 4.092880e-211 ... \n",
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"435758 5.976972e-08 2.204258e-06 0.709320 0.290680 0.000000e+00 ... \n",
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"\n",
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" lgb_4 lgb_5 lgb_6 rf_0 rf_1 rf_2 rf_3 \\\n",
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"0 1.486358e-02 5.093522e-05 6.805300e-08 0.06 0.92 0.0 0.0 \n",
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"1 0.000000e+00 0.000000e+00 0.000000e+00 0.12 0.88 0.0 0.0 \n",
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"2 4.452850e-07 7.825338e-09 1.012052e-07 0.63 0.37 0.0 0.0 \n",
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"3 0.000000e+00 0.000000e+00 8.245405e-05 0.56 0.44 0.0 0.0 \n",
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"4 0.000000e+00 0.000000e+00 2.449972e-02 0.95 0.04 0.0 0.0 \n",
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"... ... ... ... ... ... ... ... \n",
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"435754 0.000000e+00 0.000000e+00 7.871809e-06 0.97 0.03 0.0 0.0 \n",
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"435755 0.000000e+00 0.000000e+00 7.647430e-04 0.93 0.06 0.0 0.0 \n",
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"435756 0.000000e+00 0.000000e+00 7.493861e-05 0.45 0.55 0.0 0.0 \n",
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"435757 0.000000e+00 0.000000e+00 0.000000e+00 0.52 0.48 0.0 0.0 \n",
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"435758 0.000000e+00 0.000000e+00 0.000000e+00 0.87 0.13 0.0 0.0 \n",
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"\n",
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" rf_4 rf_5 rf_6 \n",
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"0 0.02 0.0 0.00 \n",
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"1 0.00 0.0 0.00 \n",
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"2 0.00 0.0 0.00 \n",
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"3 0.00 0.0 0.00 \n",
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"4 0.00 0.0 0.01 \n",
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"... ... ... ... \n",
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"435754 0.00 0.0 0.00 \n",
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"435755 0.00 0.0 0.01 \n",
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"435756 0.00 0.0 0.00 \n",
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"435757 0.00 0.0 0.00 \n",
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"435758 0.00 0.0 0.00 \n",
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"\n",
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"[435759 rows x 21 columns]\n"
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]
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"data": {
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>xgb_0</th>\n",
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" <th>xgb_1</th>\n",
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" <th>xgb_2</th>\n",
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" <th>xgb_3</th>\n",
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" <th>xgb_4</th>\n",
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" <th>xgb_5</th>\n",
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" <th>xgb_6</th>\n",
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" <th>lgb_0</th>\n",
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" <th>lgb_1</th>\n",
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" <th>lgb_2</th>\n",
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" <th>...</th>\n",
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" <th>lgb_4</th>\n",
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" <th>lgb_5</th>\n",
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" <th>lgb_6</th>\n",
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" <th>rf_0</th>\n",
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" <th>rf_1</th>\n",
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" <th>rf_2</th>\n",
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" <th>rf_3</th>\n",
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" <th>rf_4</th>\n",
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" <th>rf_5</th>\n",
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" <th>rf_6</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>0.177179</td>\n",
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" <td>0.818728</td>\n",
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" <td>2.185222e-07</td>\n",
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|
" <td>9.264143e-09</td>\n",
|
|
|
|
|
" <td>4.090067e-03</td>\n",
|
|
|
|
|
" <td>1.725062e-06</td>\n",
|
|
|
|
|
" <td>1.048052e-06</td>\n",
|
|
|
|
|
" <td>0.179625</td>\n",
|
|
|
|
|
" <td>0.804684</td>\n",
|
|
|
|
|
" <td>0.000003</td>\n",
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
" <td>1.562435e-02</td>\n",
|
|
|
|
|
" <td>6.370849e-05</td>\n",
|
|
|
|
|
" <td>1.004259e-08</td>\n",
|
|
|
|
|
" <td>0.03</td>\n",
|
|
|
|
|
" <td>0.96</td>\n",
|
|
|
|
|
" <td>0.0</td>\n",
|
|
|
|
|
" <td>0.0</td>\n",
|
|
|
|
|
" <td>0.01</td>\n",
|
|
|
|
|
" <td>0.0</td>\n",
|
|
|
|
|
" <td>0.0</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th>1</th>\n",
|
|
|
|
|
" <td>0.005155</td>\n",
|
|
|
|
|
" <td>0.994845</td>\n",
|
|
|
|
|
" <td>7.055579e-10</td>\n",
|
|
|
|
|
" <td>1.326343e-08</td>\n",
|
|
|
|
|
" <td>6.331572e-09</td>\n",
|
|
|
|
|
" <td>1.435787e-09</td>\n",
|
|
|
|
|
" <td>1.603579e-10</td>\n",
|
|
|
|
|
" <td>0.008114</td>\n",
|
|
|
|
|
" <td>0.991886</td>\n",
|
|
|
|
|
" <td>0.000000</td>\n",
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
" <td>0.000000e+00</td>\n",
|
|
|
|
|
" <td>0.000000e+00</td>\n",
|
|
|
|
|
" <td>0.000000e+00</td>\n",
|
|
|
|
|
" <td>0.13</td>\n",
|
|
|
|
|
" <td>0.87</td>\n",
|
|
|
|
|
" <td>0.0</td>\n",
|
|
|
|
|
" <td>0.0</td>\n",
|
|
|
|
|
" <td>0.00</td>\n",
|
|
|
|
|
" <td>0.0</td>\n",
|
|
|
|
|
" <td>0.0</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th>2</th>\n",
|
|
|
|
|
" <td>0.293492</td>\n",
|
|
|
|
|
" <td>0.706508</td>\n",
|
|
|
|
|
" <td>3.650662e-10</td>\n",
|
|
|
|
|
" <td>1.017633e-09</td>\n",
|
|
|
|
|
" <td>8.823530e-09</td>\n",
|
|
|
|
|
" <td>6.384080e-10</td>\n",
|
|
|
|
|
" <td>2.823794e-08</td>\n",
|
|
|
|
|
" <td>0.831445</td>\n",
|
|
|
|
|
" <td>0.168555</td>\n",
|
|
|
|
|
" <td>0.000000</td>\n",
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
" <td>4.999034e-07</td>\n",
|
|
|
|
|
" <td>4.015190e-09</td>\n",
|
|
|
|
|
" <td>4.997854e-09</td>\n",
|
|
|
|
|
" <td>0.63</td>\n",
|
|
|
|
|
" <td>0.37</td>\n",
|
|
|
|
|
" <td>0.0</td>\n",
|
|
|
|
|
" <td>0.0</td>\n",
|
|
|
|
|
" <td>0.00</td>\n",
|
|
|
|
|
" <td>0.0</td>\n",
|
|
|
|
|
" <td>0.0</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th>3</th>\n",
|
|
|
|
|
" <td>0.478112</td>\n",
|
|
|
|
|
" <td>0.521816</td>\n",
|
|
|
|
|
" <td>3.207779e-06</td>\n",
|
|
|
|
|
" <td>2.878019e-08</td>\n",
|
|
|
|
|
" <td>1.076500e-08</td>\n",
|
|
|
|
|
" <td>2.230641e-06</td>\n",
|
|
|
|
|
" <td>6.630235e-05</td>\n",
|
|
|
|
|
" <td>0.465733</td>\n",
|
|
|
|
|
" <td>0.534184</td>\n",
|
|
|
|
|
" <td>0.000000</td>\n",
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
" <td>0.000000e+00</td>\n",
|
|
|
|
|
" <td>0.000000e+00</td>\n",
|
|
|
|
|
" <td>8.245405e-05</td>\n",
|
|
|
|
|
" <td>0.55</td>\n",
|
|
|
|
|
" <td>0.45</td>\n",
|
|
|
|
|
" <td>0.0</td>\n",
|
|
|
|
|
" <td>0.0</td>\n",
|
|
|
|
|
" <td>0.00</td>\n",
|
|
|
|
|
" <td>0.0</td>\n",
|
|
|
|
|
" <td>0.0</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th>4</th>\n",
|
|
|
|
|
" <td>0.992430</td>\n",
|
|
|
|
|
" <td>0.006652</td>\n",
|
|
|
|
|
" <td>1.233117e-05</td>\n",
|
|
|
|
|
" <td>1.887496e-07</td>\n",
|
|
|
|
|
" <td>1.569583e-06</td>\n",
|
|
|
|
|
" <td>5.604260e-07</td>\n",
|
|
|
|
|
" <td>9.037877e-04</td>\n",
|
|
|
|
|
" <td>0.932050</td>\n",
|
|
|
|
|
" <td>0.043451</td>\n",
|
|
|
|
|
" <td>0.000000</td>\n",
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
" <td>0.000000e+00</td>\n",
|
|
|
|
|
" <td>0.000000e+00</td>\n",
|
|
|
|
|
" <td>2.449972e-02</td>\n",
|
|
|
|
|
" <td>0.97</td>\n",
|
|
|
|
|
" <td>0.03</td>\n",
|
|
|
|
|
" <td>0.0</td>\n",
|
|
|
|
|
" <td>0.0</td>\n",
|
|
|
|
|
" <td>0.00</td>\n",
|
|
|
|
|
" <td>0.0</td>\n",
|
|
|
|
|
" <td>0.0</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" </tbody>\n",
|
|
|
|
|
"</table>\n",
|
|
|
|
|
"<p>5 rows × 21 columns</p>\n",
|
|
|
|
|
"</div>"
|
|
|
|
|
],
|
|
|
|
|
"text/plain": [
|
|
|
|
|
" xgb_0 xgb_1 xgb_2 xgb_3 xgb_4 xgb_5 \\\n",
|
|
|
|
|
"0 0.177179 0.818728 2.185222e-07 9.264143e-09 4.090067e-03 1.725062e-06 \n",
|
|
|
|
|
"1 0.005155 0.994845 7.055579e-10 1.326343e-08 6.331572e-09 1.435787e-09 \n",
|
|
|
|
|
"2 0.293492 0.706508 3.650662e-10 1.017633e-09 8.823530e-09 6.384080e-10 \n",
|
|
|
|
|
"3 0.478112 0.521816 3.207779e-06 2.878019e-08 1.076500e-08 2.230641e-06 \n",
|
|
|
|
|
"4 0.992430 0.006652 1.233117e-05 1.887496e-07 1.569583e-06 5.604260e-07 \n",
|
|
|
|
|
"\n",
|
|
|
|
|
" xgb_6 lgb_0 lgb_1 lgb_2 ... lgb_4 \\\n",
|
|
|
|
|
"0 1.048052e-06 0.179625 0.804684 0.000003 ... 1.562435e-02 \n",
|
|
|
|
|
"1 1.603579e-10 0.008114 0.991886 0.000000 ... 0.000000e+00 \n",
|
|
|
|
|
"2 2.823794e-08 0.831445 0.168555 0.000000 ... 4.999034e-07 \n",
|
|
|
|
|
"3 6.630235e-05 0.465733 0.534184 0.000000 ... 0.000000e+00 \n",
|
|
|
|
|
"4 9.037877e-04 0.932050 0.043451 0.000000 ... 0.000000e+00 \n",
|
|
|
|
|
"\n",
|
|
|
|
|
" lgb_5 lgb_6 rf_0 rf_1 rf_2 rf_3 rf_4 rf_5 rf_6 \n",
|
|
|
|
|
"0 6.370849e-05 1.004259e-08 0.03 0.96 0.0 0.0 0.01 0.0 0.0 \n",
|
|
|
|
|
"1 0.000000e+00 0.000000e+00 0.13 0.87 0.0 0.0 0.00 0.0 0.0 \n",
|
|
|
|
|
"2 4.015190e-09 4.997854e-09 0.63 0.37 0.0 0.0 0.00 0.0 0.0 \n",
|
|
|
|
|
"3 0.000000e+00 8.245405e-05 0.55 0.45 0.0 0.0 0.00 0.0 0.0 \n",
|
|
|
|
|
"4 0.000000e+00 2.449972e-02 0.97 0.03 0.0 0.0 0.00 0.0 0.0 \n",
|
|
|
|
|
"\n",
|
|
|
|
|
"[5 rows x 21 columns]"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 26,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
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|
|
}
|
|
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|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"# 模型输出的训练集，7个特征对应7个标签的预测概率\n",
|
|
|
|
|
"print(stack_ds.X_train)"
|
|
|
|
|
"stack_ds.X_train.head()"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
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|
|
{
|
|
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|
|
"cell_type": "code",
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|
"execution_count": 59,
|
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|
|
|
"execution_count": 28,
|
|
|
|
|
"id": "835205e9",
|
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|
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"metadata": {},
|
|
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|
|
"outputs": [
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|
|
{
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"name": "stdout",
|
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"output_type": "stream",
|
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|
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"text": [
|
|
|
|
|
" xgb_0 xgb_1 xgb_2 xgb_3 xgb_4 \\\n",
|
|
|
|
|
"0 9.876224e-01 0.000789 2.774616e-06 4.129093e-07 1.311387e-06 \n",
|
|
|
|
|
"1 5.139124e-02 0.929659 1.852793e-03 1.518293e-07 1.692924e-02 \n",
|
|
|
|
|
"2 7.695035e-04 0.973729 6.878623e-04 1.573823e-07 2.408167e-02 \n",
|
|
|
|
|
"3 3.376913e-02 0.966229 2.024872e-07 7.321523e-08 1.071163e-06 \n",
|
|
|
|
|
"4 1.013981e-03 0.998553 3.794874e-06 8.755425e-08 4.243054e-04 \n",
|
|
|
|
|
"... ... ... ... ... ... \n",
|
|
|
|
|
"145248 9.615189e-01 0.038480 6.486028e-08 1.744931e-08 1.069370e-06 \n",
|
|
|
|
|
"145249 3.055384e-02 0.969440 2.475371e-07 5.530033e-08 4.299908e-06 \n",
|
|
|
|
|
"145250 8.224608e-06 0.058361 9.212288e-01 9.705171e-08 5.440121e-05 \n",
|
|
|
|
|
"145251 9.183387e-01 0.081601 5.612090e-08 1.088283e-08 5.225256e-07 \n",
|
|
|
|
|
"145252 9.203915e-07 0.003578 2.372825e-01 1.582836e-06 3.307252e-07 \n",
|
|
|
|
|
"\n",
|
|
|
|
|
" xgb_5 xgb_6 lgb_0 lgb_1 lgb_2 ... \\\n",
|
|
|
|
|
"0 7.851924e-09 1.158422e-02 0.962538 0.004222 9.599869e-23 ... \n",
|
|
|
|
|
"1 1.613036e-04 6.763449e-06 0.070947 0.882463 2.232464e-03 ... \n",
|
|
|
|
|
"2 7.296442e-04 1.884172e-06 0.004029 0.945838 1.014722e-02 ... \n",
|
|
|
|
|
"3 7.227585e-08 1.448203e-09 0.066538 0.933450 1.206630e-06 ... \n",
|
|
|
|
|
"4 4.374311e-06 4.566837e-09 0.001334 0.997391 1.580417e-06 ... \n",
|
|
|
|
|
"... ... ... ... ... ... ... \n",
|
|
|
|
|
"145248 5.049759e-08 4.010809e-07 0.917842 0.082153 2.154302e-17 ... \n",
|
|
|
|
|
"145249 1.255851e-08 1.224208e-06 0.058622 0.941370 1.332795e-12 ... \n",
|
|
|
|
|
"145250 2.034389e-02 3.132630e-06 0.000268 0.083680 8.789707e-01 ... \n",
|
|
|
|
|
"145251 2.566383e-07 5.976933e-05 0.875834 0.123030 2.631276e-12 ... \n",
|
|
|
|
|
"145252 7.591362e-01 6.988637e-08 0.000032 0.037757 2.462795e-01 ... \n",
|
|
|
|
|
"\n",
|
|
|
|
|
" lgb_4 lgb_5 lgb_6 rf_0 rf_1 rf_2 rf_3 \\\n",
|
|
|
|
|
"0 1.726260e-240 0.000000e+00 3.324009e-02 0.984 0.000 0.000 0.0 \n",
|
|
|
|
|
"1 4.396802e-02 3.888647e-04 1.077146e-08 0.106 0.816 0.038 0.0 \n",
|
|
|
|
|
"2 3.329283e-02 6.693269e-03 3.213404e-09 0.008 0.950 0.002 0.0 \n",
|
|
|
|
|
"3 9.908823e-06 1.371448e-07 1.280235e-09 0.078 0.922 0.000 0.0 \n",
|
|
|
|
|
"4 1.273086e-03 7.811306e-07 5.594472e-10 0.004 0.988 0.000 0.0 \n",
|
|
|
|
|
"... ... ... ... ... ... ... ... \n",
|
|
|
|
|
"145248 6.191352e-08 0.000000e+00 4.958509e-06 0.968 0.032 0.000 0.0 \n",
|
|
|
|
|
"145249 7.656931e-06 3.415083e-47 2.271880e-07 0.018 0.972 0.000 0.0 \n",
|
|
|
|
|
"145250 2.052535e-04 3.687570e-02 1.393421e-09 0.000 0.040 0.946 0.0 \n",
|
|
|
|
|
"145251 2.521124e-07 5.749375e-08 1.135236e-03 0.992 0.008 0.000 0.0 \n",
|
|
|
|
|
"145252 2.927400e-06 7.159244e-01 8.608624e-140 0.000 0.018 0.110 0.0 \n",
|
|
|
|
|
"\n",
|
|
|
|
|
" rf_4 rf_5 rf_6 \n",
|
|
|
|
|
"0 0.000 0.000 0.016 \n",
|
|
|
|
|
"1 0.034 0.006 0.000 \n",
|
|
|
|
|
"2 0.032 0.008 0.000 \n",
|
|
|
|
|
"3 0.000 0.000 0.000 \n",
|
|
|
|
|
"4 0.008 0.000 0.000 \n",
|
|
|
|
|
"... ... ... ... \n",
|
|
|
|
|
"145248 0.000 0.000 0.000 \n",
|
|
|
|
|
"145249 0.010 0.000 0.000 \n",
|
|
|
|
|
"145250 0.000 0.014 0.000 \n",
|
|
|
|
|
"145251 0.000 0.000 0.000 \n",
|
|
|
|
|
"145252 0.000 0.872 0.000 \n",
|
|
|
|
|
"\n",
|
|
|
|
|
"[145253 rows x 21 columns]\n"
|
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]
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"data": {
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"<div>\n",
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"<style scoped>\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
|
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" <thead>\n",
|
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|
" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
|
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" <th>xgb_0</th>\n",
|
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|
|
" <th>xgb_1</th>\n",
|
|
|
|
|
" <th>xgb_2</th>\n",
|
|
|
|
|
" <th>xgb_3</th>\n",
|
|
|
|
|
" <th>xgb_4</th>\n",
|
|
|
|
|
" <th>xgb_5</th>\n",
|
|
|
|
|
" <th>xgb_6</th>\n",
|
|
|
|
|
" <th>lgb_0</th>\n",
|
|
|
|
|
" <th>lgb_1</th>\n",
|
|
|
|
|
" <th>lgb_2</th>\n",
|
|
|
|
|
" <th>...</th>\n",
|
|
|
|
|
" <th>lgb_4</th>\n",
|
|
|
|
|
" <th>lgb_5</th>\n",
|
|
|
|
|
" <th>lgb_6</th>\n",
|
|
|
|
|
" <th>rf_0</th>\n",
|
|
|
|
|
" <th>rf_1</th>\n",
|
|
|
|
|
" <th>rf_2</th>\n",
|
|
|
|
|
" <th>rf_3</th>\n",
|
|
|
|
|
" <th>rf_4</th>\n",
|
|
|
|
|
" <th>rf_5</th>\n",
|
|
|
|
|
" <th>rf_6</th>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" </thead>\n",
|
|
|
|
|
" <tbody>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th>0</th>\n",
|
|
|
|
|
" <td>0.991493</td>\n",
|
|
|
|
|
" <td>0.000210</td>\n",
|
|
|
|
|
" <td>4.796058e-06</td>\n",
|
|
|
|
|
" <td>6.178684e-08</td>\n",
|
|
|
|
|
" <td>6.947614e-07</td>\n",
|
|
|
|
|
" <td>2.410490e-09</td>\n",
|
|
|
|
|
" <td>8.291459e-03</td>\n",
|
|
|
|
|
" <td>0.000000</td>\n",
|
|
|
|
|
" <td>0.000000</td>\n",
|
|
|
|
|
" <td>0.000000</td>\n",
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
" <td>0.000000e+00</td>\n",
|
|
|
|
|
" <td>0.000000</td>\n",
|
|
|
|
|
" <td>1.000000e+00</td>\n",
|
|
|
|
|
" <td>0.99</td>\n",
|
|
|
|
|
" <td>0.00</td>\n",
|
|
|
|
|
" <td>0.00</td>\n",
|
|
|
|
|
" <td>0.0</td>\n",
|
|
|
|
|
" <td>0.00</td>\n",
|
|
|
|
|
" <td>0.00</td>\n",
|
|
|
|
|
" <td>0.01</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th>1</th>\n",
|
|
|
|
|
" <td>0.024731</td>\n",
|
|
|
|
|
" <td>0.964372</td>\n",
|
|
|
|
|
" <td>6.387765e-04</td>\n",
|
|
|
|
|
" <td>4.205048e-08</td>\n",
|
|
|
|
|
" <td>1.006575e-02</td>\n",
|
|
|
|
|
" <td>1.879628e-04</td>\n",
|
|
|
|
|
" <td>4.830114e-06</td>\n",
|
|
|
|
|
" <td>0.065073</td>\n",
|
|
|
|
|
" <td>0.877354</td>\n",
|
|
|
|
|
" <td>0.001678</td>\n",
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
" <td>5.530599e-02</td>\n",
|
|
|
|
|
" <td>0.000589</td>\n",
|
|
|
|
|
" <td>8.601484e-11</td>\n",
|
|
|
|
|
" <td>0.09</td>\n",
|
|
|
|
|
" <td>0.80</td>\n",
|
|
|
|
|
" <td>0.05</td>\n",
|
|
|
|
|
" <td>0.0</td>\n",
|
|
|
|
|
" <td>0.06</td>\n",
|
|
|
|
|
" <td>0.00</td>\n",
|
|
|
|
|
" <td>0.00</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th>2</th>\n",
|
|
|
|
|
" <td>0.000780</td>\n",
|
|
|
|
|
" <td>0.979776</td>\n",
|
|
|
|
|
" <td>8.593459e-04</td>\n",
|
|
|
|
|
" <td>1.267791e-07</td>\n",
|
|
|
|
|
" <td>1.710379e-02</td>\n",
|
|
|
|
|
" <td>1.477527e-03</td>\n",
|
|
|
|
|
" <td>2.521008e-06</td>\n",
|
|
|
|
|
" <td>0.005164</td>\n",
|
|
|
|
|
" <td>0.933849</td>\n",
|
|
|
|
|
" <td>0.016355</td>\n",
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
" <td>3.657553e-02</td>\n",
|
|
|
|
|
" <td>0.008057</td>\n",
|
|
|
|
|
" <td>0.000000e+00</td>\n",
|
|
|
|
|
" <td>0.01</td>\n",
|
|
|
|
|
" <td>0.97</td>\n",
|
|
|
|
|
" <td>0.00</td>\n",
|
|
|
|
|
" <td>0.0</td>\n",
|
|
|
|
|
" <td>0.01</td>\n",
|
|
|
|
|
" <td>0.01</td>\n",
|
|
|
|
|
" <td>0.00</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th>3</th>\n",
|
|
|
|
|
" <td>0.042695</td>\n",
|
|
|
|
|
" <td>0.957304</td>\n",
|
|
|
|
|
" <td>2.283268e-08</td>\n",
|
|
|
|
|
" <td>4.387427e-08</td>\n",
|
|
|
|
|
" <td>4.175481e-07</td>\n",
|
|
|
|
|
" <td>4.406019e-08</td>\n",
|
|
|
|
|
" <td>6.909629e-10</td>\n",
|
|
|
|
|
" <td>0.054392</td>\n",
|
|
|
|
|
" <td>0.945608</td>\n",
|
|
|
|
|
" <td>0.000000</td>\n",
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
" <td>4.285638e-08</td>\n",
|
|
|
|
|
" <td>0.000000</td>\n",
|
|
|
|
|
" <td>0.000000e+00</td>\n",
|
|
|
|
|
" <td>0.04</td>\n",
|
|
|
|
|
" <td>0.96</td>\n",
|
|
|
|
|
" <td>0.00</td>\n",
|
|
|
|
|
" <td>0.0</td>\n",
|
|
|
|
|
" <td>0.00</td>\n",
|
|
|
|
|
" <td>0.00</td>\n",
|
|
|
|
|
" <td>0.00</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" <tr>\n",
|
|
|
|
|
" <th>4</th>\n",
|
|
|
|
|
" <td>0.000457</td>\n",
|
|
|
|
|
" <td>0.999334</td>\n",
|
|
|
|
|
" <td>3.366338e-06</td>\n",
|
|
|
|
|
" <td>4.893879e-08</td>\n",
|
|
|
|
|
" <td>2.045808e-04</td>\n",
|
|
|
|
|
" <td>7.889498e-07</td>\n",
|
|
|
|
|
" <td>1.415576e-09</td>\n",
|
|
|
|
|
" <td>0.001367</td>\n",
|
|
|
|
|
" <td>0.995857</td>\n",
|
|
|
|
|
" <td>0.000000</td>\n",
|
|
|
|
|
" <td>...</td>\n",
|
|
|
|
|
" <td>2.776106e-03</td>\n",
|
|
|
|
|
" <td>0.000000</td>\n",
|
|
|
|
|
" <td>0.000000e+00</td>\n",
|
|
|
|
|
" <td>0.00</td>\n",
|
|
|
|
|
" <td>0.99</td>\n",
|
|
|
|
|
" <td>0.00</td>\n",
|
|
|
|
|
" <td>0.0</td>\n",
|
|
|
|
|
" <td>0.01</td>\n",
|
|
|
|
|
" <td>0.00</td>\n",
|
|
|
|
|
" <td>0.00</td>\n",
|
|
|
|
|
" </tr>\n",
|
|
|
|
|
" </tbody>\n",
|
|
|
|
|
"</table>\n",
|
|
|
|
|
"<p>5 rows × 21 columns</p>\n",
|
|
|
|
|
"</div>"
|
|
|
|
|
],
|
|
|
|
|
"text/plain": [
|
|
|
|
|
" xgb_0 xgb_1 xgb_2 xgb_3 xgb_4 xgb_5 \\\n",
|
|
|
|
|
"0 0.991493 0.000210 4.796058e-06 6.178684e-08 6.947614e-07 2.410490e-09 \n",
|
|
|
|
|
"1 0.024731 0.964372 6.387765e-04 4.205048e-08 1.006575e-02 1.879628e-04 \n",
|
|
|
|
|
"2 0.000780 0.979776 8.593459e-04 1.267791e-07 1.710379e-02 1.477527e-03 \n",
|
|
|
|
|
"3 0.042695 0.957304 2.283268e-08 4.387427e-08 4.175481e-07 4.406019e-08 \n",
|
|
|
|
|
"4 0.000457 0.999334 3.366338e-06 4.893879e-08 2.045808e-04 7.889498e-07 \n",
|
|
|
|
|
"\n",
|
|
|
|
|
" xgb_6 lgb_0 lgb_1 lgb_2 ... lgb_4 lgb_5 \\\n",
|
|
|
|
|
"0 8.291459e-03 0.000000 0.000000 0.000000 ... 0.000000e+00 0.000000 \n",
|
|
|
|
|
"1 4.830114e-06 0.065073 0.877354 0.001678 ... 5.530599e-02 0.000589 \n",
|
|
|
|
|
"2 2.521008e-06 0.005164 0.933849 0.016355 ... 3.657553e-02 0.008057 \n",
|
|
|
|
|
"3 6.909629e-10 0.054392 0.945608 0.000000 ... 4.285638e-08 0.000000 \n",
|
|
|
|
|
"4 1.415576e-09 0.001367 0.995857 0.000000 ... 2.776106e-03 0.000000 \n",
|
|
|
|
|
"\n",
|
|
|
|
|
" lgb_6 rf_0 rf_1 rf_2 rf_3 rf_4 rf_5 rf_6 \n",
|
|
|
|
|
"0 1.000000e+00 0.99 0.00 0.00 0.0 0.00 0.00 0.01 \n",
|
|
|
|
|
"1 8.601484e-11 0.09 0.80 0.05 0.0 0.06 0.00 0.00 \n",
|
|
|
|
|
"2 0.000000e+00 0.01 0.97 0.00 0.0 0.01 0.01 0.00 \n",
|
|
|
|
|
"3 0.000000e+00 0.04 0.96 0.00 0.0 0.00 0.00 0.00 \n",
|
|
|
|
|
"4 0.000000e+00 0.00 0.99 0.00 0.0 0.01 0.00 0.00 \n",
|
|
|
|
|
"\n",
|
|
|
|
|
"[5 rows x 21 columns]"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
"execution_count": 28,
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"output_type": "execute_result"
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"# 模型输出的测试集，7个特征对应7个标签的预测概率\n",
|
|
|
|
|
"print(stack_ds.X_test) "
|
|
|
|
|
"stack_ds.X_test.head()"
|
|
|
|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 60,
|
|
|
|
|
"execution_count": 30,
|
|
|
|
|
"id": "b9db35dc",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [],
|
|
|
|
|
"outputs": [
|
|
|
|
|
{
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
"Wall time: 1min 53s\n"
|
|
|
|
|
]
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
"source": [
|
|
|
|
|
"%%time\n",
|
|
|
|
|
"# 用lr做最后一层\n",
|
|
|
|
|
"stacker = Classifier(dataset=stack_ds, estimator=LogisticRegression, parameters={\"solver\": 'lbfgs', \"max_iter\": 1000},use_cache=False)\n",
|
|
|
|
|
"predict_stack = stacker.predict()"
|
|
|
|
|
@ -507,7 +807,7 @@
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 64,
|
|
|
|
|
"execution_count": 32,
|
|
|
|
|
"id": "a4a48219",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
@ -515,19 +815,19 @@
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
"[[9.95173402e-01 2.67623709e-03 4.23846755e-08 ... 3.15435935e-05\n",
|
|
|
|
|
" 5.66194220e-06 2.11044140e-03]\n",
|
|
|
|
|
" [2.23612439e-02 9.70927685e-01 1.23929922e-03 ... 4.49727904e-03\n",
|
|
|
|
|
" 8.73983383e-04 9.97020226e-05]\n",
|
|
|
|
|
" [6.22588197e-03 9.89402233e-01 9.81655972e-04 ... 2.83331258e-03\n",
|
|
|
|
|
" 5.22139184e-04 3.45071569e-05]\n",
|
|
|
|
|
"[[9.99137967e-01 4.76574513e-04 3.32764186e-07 ... 3.03237700e-06\n",
|
|
|
|
|
" 1.62161211e-06 3.79326620e-04]\n",
|
|
|
|
|
" [2.00732175e-02 9.71682830e-01 1.51484266e-03 ... 5.69138554e-03\n",
|
|
|
|
|
" 9.40725428e-04 9.60822625e-05]\n",
|
|
|
|
|
" [5.54556002e-03 9.91048437e-01 8.04840682e-04 ... 2.11437934e-03\n",
|
|
|
|
|
" 4.56463787e-04 3.00919502e-05]\n",
|
|
|
|
|
" ...\n",
|
|
|
|
|
" [5.36335125e-06 2.06267200e-03 9.90604140e-01 ... 8.55252386e-04\n",
|
|
|
|
|
" 4.18405061e-03 1.64678945e-05]\n",
|
|
|
|
|
" [9.96602824e-01 2.15991442e-03 7.27481581e-08 ... 3.63552051e-05\n",
|
|
|
|
|
" 6.80942632e-06 1.19199377e-03]\n",
|
|
|
|
|
" [5.89156494e-05 1.15333400e-03 1.09178439e-02 ... 3.09244417e-04\n",
|
|
|
|
|
" 9.85167196e-01 2.21261408e-05]]\n"
|
|
|
|
|
" [4.60179790e-06 1.78298095e-03 9.91553958e-01 ... 7.26752933e-04\n",
|
|
|
|
|
" 3.79135124e-03 1.53584401e-05]\n",
|
|
|
|
|
" [9.96307096e-01 2.43558944e-03 1.01596361e-07 ... 3.94985596e-05\n",
|
|
|
|
|
" 7.41569805e-06 1.20819024e-03]\n",
|
|
|
|
|
" [5.34671504e-05 7.62534718e-04 5.58323657e-03 ... 2.11410908e-04\n",
|
|
|
|
|
" 9.91805379e-01 1.69502656e-05]]\n"
|
|
|
|
|
]
|
|
|
|
|
}
|
|
|
|
|
],
|
|
|
|
|
@ -553,7 +853,7 @@
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "code",
|
|
|
|
|
"execution_count": 65,
|
|
|
|
|
"execution_count": 33,
|
|
|
|
|
"id": "a28806a0",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"outputs": [
|
|
|
|
|
@ -561,9 +861,9 @@
|
|
|
|
|
"name": "stdout",
|
|
|
|
|
"output_type": "stream",
|
|
|
|
|
"text": [
|
|
|
|
|
"0.9284696357390209\n",
|
|
|
|
|
"0.8890005714167694\n",
|
|
|
|
|
"0.9511404239499356\n"
|
|
|
|
|
"0.9254473229468583\n",
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"0.8412562907478676\n",
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"0.9535087055000585\n"
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]
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}
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],
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@ -573,6 +873,28 @@
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"print(accuracy_score(np.argmax(stack_ds.X_test.iloc[:, 14:].values, axis=1),y_test)) # RF"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 35,
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"id": "1db92fec",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.9537840870756542\n",
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"Wall time: 50.8 s\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"# 测试单模运行结果是否一致\n",
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"rf_predict = rf_model(X_train, y_train, X_test, None)\n",
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"print(accuracy_score(np.argmax(rf_predict, axis=1),y_test)) # RF"
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]
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},
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{
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"cell_type": "markdown",
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"id": "2e9423ce",
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@ -583,7 +905,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 70,
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"execution_count": 37,
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"id": "d2b50ba4",
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"metadata": {},
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"outputs": [
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@ -591,8 +913,8 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"主观根据结果blending： 0.9425209806337908\n",
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"根据最优权重的blending： 0.9488616414118813\n"
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"主观根据结果blending： 0.9504106627746071\n",
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"根据最优权重的线性加权： 0.9530749795184953\n"
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]
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}
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],
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@ -603,19 +925,74 @@
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"rf_t = stack_ds.X_test.iloc[:, 14:].values\n",
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"\n",
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"# 根据分数好坏随机定\n",
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"result = 0.3*xgb_t+0.2*lgb_t+0.5*rf_t\n",
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"result = 0.2*xgb_t+0.1*lgb_t+0.7*rf_t\n",
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"print('主观根据结果blending：', accuracy_score(np.argmax(result, axis=1), y_test))\n",
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"# 根据上面提供的最优权重 Best Weights: [0.36556831 0.00303401 0.63139768]\n",
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"result = 0.36556831*xgb_t+0.00303401*lgb_t+0.63139768*rf_t\n",
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"print('根据最优权重的blending：',accuracy_score(np.argmax(result, axis=1), y_test))"
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"# 根据上面提供的最优权重 Best Weights: [2.53464919e-01 1.48562205e-20 7.46535081e-01]\n",
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"result = 2.53464919e-01*xgb_t+1.48562205e-20*lgb_t+7.46535081e-01*rf_t\n",
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"print('根据最优权重的线性加权：',accuracy_score(np.argmax(result, axis=1), y_test))"
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]
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},
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{
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"cell_type": "markdown",
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"id": "e8ce00b2",
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"metadata": {},
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"source": [
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"可以观察到最优权重比我们主观选权重更优，单对比单模结果反而下降了"
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]
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},
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{
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"cell_type": "markdown",
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"id": "d4a12d6d",
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"metadata": {},
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"source": [
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"### Blending的分数"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "ffe1cf4d",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Wall time: 14min 10s\n"
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]
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}
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],
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"source": [
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"%%time\n",
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"blend_ds = pipeline.blend(seed=111)\n",
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"blender = Classifier(dataset=blend_ds, estimator=LogisticRegression, parameters={\"solver\": 'lbfgs', \"max_iter\": 1000},use_cache=False)\n",
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"predict_blend = blender.predict()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 19,
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"id": "506adffb",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
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|
"0.9546859617357301\n"
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]
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}
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],
|
|
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"source": [
|
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|
"print(accuracy_score(np.argmax(predict_blend, axis=1), y_test))"
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]
|
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},
|
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{
|
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|
"cell_type": "markdown",
|
|
|
|
|
"id": "dfec8968",
|
|
|
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|
"id": "e84c19ae",
|
|
|
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|
"metadata": {},
|
|
|
|
|
"source": [
|
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|
|
|
"可以观察到最优权重比我们主观选权重更优"
|
|
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|
|
"使用Blending的分数有所提升"
|
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]
|
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},
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{
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@ -623,12 +1000,12 @@
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"id": "e8daf1e3",
|
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|
"metadata": {},
|
|
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"source": [
|
|
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|
|
"### stacking的分数"
|
|
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|
|
"### Stacking的分数"
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|
]
|
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|
},
|
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|
{
|
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|
"cell_type": "code",
|
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|
|
|
"execution_count": 71,
|
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|
"execution_count": 38,
|
|
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|
|
"id": "4930b407",
|
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"metadata": {},
|
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"outputs": [
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@ -636,7 +1013,7 @@
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"name": "stdout",
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"output_type": "stream",
|
|
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|
|
"text": [
|
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|
"0.957439777491687\n"
|
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|
|
"0.9589887988544127\n"
|
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]
|
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}
|
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],
|
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|
@ -644,12 +1021,20 @@
|
|
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|
|
"print(accuracy_score(np.argmax(predict_stack, axis=1), y_test))"
|
|
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|
|
]
|
|
|
|
|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
"id": "d5ad3c4b",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"source": [
|
|
|
|
|
"可以明显看到提升的效果"
|
|
|
|
|
]
|
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|
},
|
|
|
|
|
{
|
|
|
|
|
"cell_type": "markdown",
|
|
|
|
|
"id": "6311fbd7",
|
|
|
|
|
"metadata": {},
|
|
|
|
|
"source": [
|
|
|
|
|
"## 再说结论，该数据集（fetch_covtype）Stacking的方法更好"
|
|
|
|
|
"## 再说结论，该数据集（fetch_covtype）Stacking的方法相比Blending和线性加权更好"
|
|
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|
]
|
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|
}
|
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|
],
|
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|
benjas
4 years ago