msb-public-java
/
AiLearning-Theory-Applying
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benjas 5 years ago
parent 340e19356d
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365
机器学习竞赛实战_优胜解决方案/工业化工生产预测/.ipynb_checkpoints/工业生产预测-checkpoint.ipynb
Unescape View File

@ -334,9 +334,374 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"### 如何确定字段需要处理\n",
"我们需要解决一些异常值，如某值相对其它值过大的离群点" "我们需要解决一些异常值，如某值相对其它值过大的离群点"
] ]
}, },
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
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"text/plain": [
" A1 A2 A3 A4 A6 A8 \\\n",
"count 1396.000000 42.0 1354.000000 1396.000000 1396.000000 149.000000 \n",
"mean 298.853868 125.0 403.515510 705.974212 28.287751 78.818792 \n",
"std 10.130552 0.0 13.348093 53.214754 6.742765 2.683920 \n",
"min 200.000000 125.0 270.000000 470.000000 17.000000 70.000000 \n",
"25% 300.000000 125.0 405.000000 700.000000 24.000000 80.000000 \n",
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"75% 300.000000 125.0 405.000000 700.000000 30.000000 80.000000 \n",
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" A10 A12 A13 A15 A17 \\\n",
"count 1396.000000 1396.000000 1396.000000 1396.000000 1396.000000 \n",
"mean 100.861032 102.641834 0.199907 103.829370 104.766905 \n",
"std 0.905198 0.915387 0.002524 0.963639 1.401446 \n",
"min 100.000000 98.000000 0.120000 100.000000 89.000000 \n",
"25% 100.000000 102.000000 0.200000 103.000000 104.000000 \n",
"50% 101.000000 103.000000 0.200000 104.000000 105.000000 \n",
"75% 102.000000 103.000000 0.200000 104.000000 105.000000 \n",
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"\n",
" A18 A19 A21 A22 A23 \\\n",
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"mean 0.199928 231.067335 48.707825 9.117120 5.002872 \n",
"std 0.002676 50.478071 4.976531 0.369152 0.136638 \n",
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"\n",
" A27 B1 B2 B3 B6 \\\n",
"count 1396.000000 1386.000000 1394.000000 1394.000000 1396.000000 \n",
"mean 74.396848 334.452742 3.454412 3.500072 72.065186 \n",
"std 3.044490 105.120753 0.388585 0.002678 9.161986 \n",
"min 45.000000 3.500000 0.150000 3.500000 40.000000 \n",
"25% 73.000000 320.000000 3.500000 3.500000 65.000000 \n",
"50% 73.000000 320.000000 3.500000 3.500000 78.000000 \n",
"75% 77.000000 330.000000 3.500000 3.500000 80.000000 \n",
"max 80.000000 1200.000000 3.600000 3.600000 80.000000 \n",
"\n",
" B8 B12 B13 B14 收率 \n",
"count 1395.000000 1395.000000 1395.000000 1396.000000 1396.000000 \n",
"mean 43.709677 1020.215054 0.149419 410.403295 0.923244 \n",
"std 4.338396 205.920155 0.008213 26.018410 0.030880 \n",
"min 20.000000 400.000000 0.030000 40.000000 0.624000 \n",
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"source": [
"# pd.set_option('display.max_rows',100)#设置最大可见100行\n",
"pd.set_option('display.max_columns',100) #给最大列设置为100列\n",
"df_trn.describe()"
]
},
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"cell_type": "markdown",
"metadata": {},
"source": [
"**观测点：**\n",
"<ul>\n",
" <li>A5、A9等字段的describe没有了，而head()是有的，说明这些字段有问题\n",
" <li>理论上，std（方差也可以）越大表明特征间的差异越大，这样模型能学到区分性，但是过大可能是数据有离群值，B1、B12是需要关注的，再看其它值，B1里面最小值是3.5，25%/50%/75%都是320，3.5非常离群，而B12里最小值和中位数和最大值像是递进。"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 5,

365
机器学习竞赛实战_优胜解决方案/工业化工生产预测/工业生产预测.ipynb
Unescape View File

@ -334,9 +334,374 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"### 如何确定字段需要处理\n",
"我们需要解决一些异常值，如某值相对其它值过大的离群点" "我们需要解决一些异常值，如某值相对其它值过大的离群点"
] ]
}, },
{
"cell_type": "code",
"execution_count": 5,
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" <td>3.500000</td>\n",
" <td>0.150000</td>\n",
" <td>3.500000</td>\n",
" <td>40.000000</td>\n",
" <td>20.000000</td>\n",
" <td>400.000000</td>\n",
" <td>0.030000</td>\n",
" <td>40.000000</td>\n",
" <td>0.624000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>300.000000</td>\n",
" <td>125.0</td>\n",
" <td>405.000000</td>\n",
" <td>700.000000</td>\n",
" <td>24.000000</td>\n",
" <td>80.000000</td>\n",
" <td>100.000000</td>\n",
" <td>102.000000</td>\n",
" <td>0.200000</td>\n",
" <td>103.000000</td>\n",
" <td>104.000000</td>\n",
" <td>0.200000</td>\n",
" <td>200.000000</td>\n",
" <td>50.000000</td>\n",
" <td>9.000000</td>\n",
" <td>5.000000</td>\n",
" <td>73.000000</td>\n",
" <td>320.000000</td>\n",
" <td>3.500000</td>\n",
" <td>3.500000</td>\n",
" <td>65.000000</td>\n",
" <td>45.000000</td>\n",
" <td>800.000000</td>\n",
" <td>0.150000</td>\n",
" <td>400.000000</td>\n",
" <td>0.902000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>300.000000</td>\n",
" <td>125.0</td>\n",
" <td>405.000000</td>\n",
" <td>700.000000</td>\n",
" <td>29.000000</td>\n",
" <td>80.000000</td>\n",
" <td>101.000000</td>\n",
" <td>103.000000</td>\n",
" <td>0.200000</td>\n",
" <td>104.000000</td>\n",
" <td>105.000000</td>\n",
" <td>0.200000</td>\n",
" <td>200.000000</td>\n",
" <td>50.000000</td>\n",
" <td>9.000000</td>\n",
" <td>5.000000</td>\n",
" <td>73.000000</td>\n",
" <td>320.000000</td>\n",
" <td>3.500000</td>\n",
" <td>3.500000</td>\n",
" <td>78.000000</td>\n",
" <td>45.000000</td>\n",
" <td>1200.000000</td>\n",
" <td>0.150000</td>\n",
" <td>400.000000</td>\n",
" <td>0.925000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>300.000000</td>\n",
" <td>125.0</td>\n",
" <td>405.000000</td>\n",
" <td>700.000000</td>\n",
" <td>30.000000</td>\n",
" <td>80.000000</td>\n",
" <td>102.000000</td>\n",
" <td>103.000000</td>\n",
" <td>0.200000</td>\n",
" <td>104.000000</td>\n",
" <td>105.000000</td>\n",
" <td>0.200000</td>\n",
" <td>300.000000</td>\n",
" <td>50.000000</td>\n",
" <td>9.000000</td>\n",
" <td>5.000000</td>\n",
" <td>77.000000</td>\n",
" <td>330.000000</td>\n",
" <td>3.500000</td>\n",
" <td>3.500000</td>\n",
" <td>80.000000</td>\n",
" <td>45.000000</td>\n",
" <td>1200.000000</td>\n",
" <td>0.150000</td>\n",
" <td>420.000000</td>\n",
" <td>0.943000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>300.000000</td>\n",
" <td>125.0</td>\n",
" <td>405.000000</td>\n",
" <td>980.000000</td>\n",
" <td>97.000000</td>\n",
" <td>82.000000</td>\n",
" <td>103.000000</td>\n",
" <td>107.000000</td>\n",
" <td>0.200000</td>\n",
" <td>109.000000</td>\n",
" <td>108.000000</td>\n",
" <td>0.200000</td>\n",
" <td>350.000000</td>\n",
" <td>90.000000</td>\n",
" <td>10.000000</td>\n",
" <td>10.000000</td>\n",
" <td>80.000000</td>\n",
" <td>1200.000000</td>\n",
" <td>3.600000</td>\n",
" <td>3.600000</td>\n",
" <td>80.000000</td>\n",
" <td>73.000000</td>\n",
" <td>1200.000000</td>\n",
" <td>0.150000</td>\n",
" <td>460.000000</td>\n",
" <td>1.000800</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A1 A2 A3 A4 A6 A8 \\\n",
"count 1396.000000 42.0 1354.000000 1396.000000 1396.000000 149.000000 \n",
"mean 298.853868 125.0 403.515510 705.974212 28.287751 78.818792 \n",
"std 10.130552 0.0 13.348093 53.214754 6.742765 2.683920 \n",
"min 200.000000 125.0 270.000000 470.000000 17.000000 70.000000 \n",
"25% 300.000000 125.0 405.000000 700.000000 24.000000 80.000000 \n",
"50% 300.000000 125.0 405.000000 700.000000 29.000000 80.000000 \n",
"75% 300.000000 125.0 405.000000 700.000000 30.000000 80.000000 \n",
"max 300.000000 125.0 405.000000 980.000000 97.000000 82.000000 \n",
"\n",
" A10 A12 A13 A15 A17 \\\n",
"count 1396.000000 1396.000000 1396.000000 1396.000000 1396.000000 \n",
"mean 100.861032 102.641834 0.199907 103.829370 104.766905 \n",
"std 0.905198 0.915387 0.002524 0.963639 1.401446 \n",
"min 100.000000 98.000000 0.120000 100.000000 89.000000 \n",
"25% 100.000000 102.000000 0.200000 103.000000 104.000000 \n",
"50% 101.000000 103.000000 0.200000 104.000000 105.000000 \n",
"75% 102.000000 103.000000 0.200000 104.000000 105.000000 \n",
"max 103.000000 107.000000 0.200000 109.000000 108.000000 \n",
"\n",
" A18 A19 A21 A22 A23 \\\n",
"count 1396.000000 1396.000000 1393.000000 1396.000000 1393.000000 \n",
"mean 0.199928 231.067335 48.707825 9.117120 5.002872 \n",
"std 0.002676 50.478071 4.976531 0.369152 0.136638 \n",
"min 0.100000 100.000000 20.000000 3.500000 4.000000 \n",
"25% 0.200000 200.000000 50.000000 9.000000 5.000000 \n",
"50% 0.200000 200.000000 50.000000 9.000000 5.000000 \n",
"75% 0.200000 300.000000 50.000000 9.000000 5.000000 \n",
"max 0.200000 350.000000 90.000000 10.000000 10.000000 \n",
"\n",
" A27 B1 B2 B3 B6 \\\n",
"count 1396.000000 1386.000000 1394.000000 1394.000000 1396.000000 \n",
"mean 74.396848 334.452742 3.454412 3.500072 72.065186 \n",
"std 3.044490 105.120753 0.388585 0.002678 9.161986 \n",
"min 45.000000 3.500000 0.150000 3.500000 40.000000 \n",
"25% 73.000000 320.000000 3.500000 3.500000 65.000000 \n",
"50% 73.000000 320.000000 3.500000 3.500000 78.000000 \n",
"75% 77.000000 330.000000 3.500000 3.500000 80.000000 \n",
"max 80.000000 1200.000000 3.600000 3.600000 80.000000 \n",
"\n",
" B8 B12 B13 B14 收率 \n",
"count 1395.000000 1395.000000 1395.000000 1396.000000 1396.000000 \n",
"mean 43.709677 1020.215054 0.149419 410.403295 0.923244 \n",
"std 4.338396 205.920155 0.008213 26.018410 0.030880 \n",
"min 20.000000 400.000000 0.030000 40.000000 0.624000 \n",
"25% 45.000000 800.000000 0.150000 400.000000 0.902000 \n",
"50% 45.000000 1200.000000 0.150000 400.000000 0.925000 \n",
"75% 45.000000 1200.000000 0.150000 420.000000 0.943000 \n",
"max 73.000000 1200.000000 0.150000 460.000000 1.000800 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# pd.set_option('display.max_rows',100)#设置最大可见100行\n",
"pd.set_option('display.max_columns',100) #给最大列设置为100列\n",
"df_trn.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**观测点：**\n",
"<ul>\n",
" <li>A5、A9等字段的describe没有了，而head()是有的，说明这些字段有问题\n",
" <li>理论上，std（方差也可以）越大表明特征间的差异越大，这样模型能学到区分性，但是过大可能是数据有离群值，B1、B12是需要关注的，再看其它值，B1里面最小值是3.5，25%/50%/75%都是320，3.5非常离群，而B12里最小值和中位数和最大值像是递进。"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 5,

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