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238 lines
10 KiB
238 lines
10 KiB
# Vihesabu vya vyakula 2
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Katika somo hili la pili la uainishaji, utaangalia njia zaidi za kuainisha data za nambari. Pia utajifunza kuhusu athari za kuchagua kihesabu kimoja juu ya kingine.
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## [Jaribio la kabla ya somo](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/23/)
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### Sharti
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Tunadhani kwamba umekamilisha masomo ya awali na una dataset iliyosafishwa katika folda yako ya `data` inayoitwa _cleaned_cuisines.csv_ katika mzizi wa folda hii ya masomo manne.
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### Maandalizi
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Tumeweka faili yako ya _notebook.ipynb_ na dataset iliyosafishwa na tumeigawa katika dataframes za X na y, tayari kwa mchakato wa kujenga modeli.
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## Ramani ya uainishaji
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Hapo awali, ulijifunza kuhusu chaguzi mbalimbali unazoweza kuwa nazo unapoweka data kwa kutumia karatasi ya udanganyifu ya Microsoft. Scikit-learn inatoa karatasi ya udanganyifu inayofanana, lakini yenye undani zaidi ambayo inaweza kusaidia zaidi kupunguza washauri wako (neno lingine la vihesabu):
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> Tip: [tembelea ramani hii mtandaoni](https://scikit-learn.org/stable/tutorial/machine_learning_map/) na bonyeza njia ili kusoma nyaraka.
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### Mpango
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Ramani hii ni muhimu sana mara tu unapokuwa na uelewa mzuri wa data yako, kwani unaweza 'kutembea' kwenye njia zake hadi uamuzi:
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- Tuna sampuli >50
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- Tunataka kutabiri kategoria
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- Tuna data zilizo na lebo
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- Tuna sampuli chini ya 100K
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- ✨ Tunaweza kuchagua Linear SVC
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- Ikiwa hiyo haifanyi kazi, kwa kuwa tuna data za nambari
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- Tunaweza kujaribu ✨ KNeighbors Classifier
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- Ikiwa hiyo haifanyi kazi, jaribu ✨ SVC na ✨ Ensemble Classifiers
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Hii ni njia nzuri sana ya kufuata.
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## Zoezi - gawanya data
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Kufuata njia hii, tunapaswa kuanza kwa kuingiza baadhi ya maktaba za kutumia.
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1. Ingiza maktaba zinazohitajika:
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```python
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.linear_model import LogisticRegression
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from sklearn.svm import SVC
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from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
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from sklearn.model_selection import train_test_split, cross_val_score
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from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve
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import numpy as np
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```
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1. Gawanya data yako ya mafunzo na majaribio:
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```python
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X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)
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```
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## Kihesabu cha Linear SVC
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Support-Vector clustering (SVC) ni mtoto wa familia ya Support-Vector machines ya mbinu za ML (jifunze zaidi kuhusu hizi hapa chini). Katika njia hii, unaweza kuchagua 'kernel' kuamua jinsi ya kuainisha lebo. Kigezo cha 'C' kinahusu 'uregulishaji' ambao unadhibiti ushawishi wa vigezo. Kernel inaweza kuwa moja ya [kadhaa](https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC); hapa tunaweka 'linear' kuhakikisha tunatumia linear SVC. Uwezekano kwa default ni 'false'; hapa tunaweka 'true' ili kupata makadirio ya uwezekano. Tunaweka hali ya nasibu kuwa '0' ili kuchanganya data kupata uwezekano.
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### Zoezi - tumia linear SVC
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Anza kwa kuunda safu ya vihesabu. Utaongeza polepole kwenye safu hii tunapojaribu.
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1. Anza na Linear SVC:
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```python
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C = 10
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# Create different classifiers.
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classifiers = {
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'Linear SVC': SVC(kernel='linear', C=C, probability=True,random_state=0)
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}
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```
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2. Funza modeli yako kwa kutumia Linear SVC na uchapishe ripoti:
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```python
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n_classifiers = len(classifiers)
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for index, (name, classifier) in enumerate(classifiers.items()):
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classifier.fit(X_train, np.ravel(y_train))
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y_pred = classifier.predict(X_test)
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accuracy = accuracy_score(y_test, y_pred)
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print("Accuracy (train) for %s: %0.1f%% " % (name, accuracy * 100))
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print(classification_report(y_test,y_pred))
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```
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Matokeo ni mazuri sana:
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```output
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Accuracy (train) for Linear SVC: 78.6%
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precision recall f1-score support
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chinese 0.71 0.67 0.69 242
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indian 0.88 0.86 0.87 234
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japanese 0.79 0.74 0.76 254
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korean 0.85 0.81 0.83 242
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thai 0.71 0.86 0.78 227
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accuracy 0.79 1199
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macro avg 0.79 0.79 0.79 1199
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weighted avg 0.79 0.79 0.79 1199
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```
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## K-Neighbors classifier
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K-Neighbors ni sehemu ya familia ya "majirani" ya mbinu za ML, ambazo zinaweza kutumika kwa kujifunza kwa usimamizi na bila usimamizi. Katika njia hii, idadi iliyopangwa ya pointi huundwa na data hukusanywa karibu na pointi hizi ili lebo zilizojumlishwa ziweze kutabiriwa kwa data.
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### Zoezi - tumia K-Neighbors classifier
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Kihesabu kilichopita kilikuwa kizuri, na kilifanya kazi vizuri na data, lakini labda tunaweza kupata usahihi bora zaidi. Jaribu K-Neighbors classifier.
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1. Ongeza mstari kwenye safu yako ya kihesabu (ongeza koma baada ya kipengee cha Linear SVC):
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```python
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'KNN classifier': KNeighborsClassifier(C),
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```
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Matokeo ni mabaya kidogo:
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```output
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Accuracy (train) for KNN classifier: 73.8%
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precision recall f1-score support
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chinese 0.64 0.67 0.66 242
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indian 0.86 0.78 0.82 234
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japanese 0.66 0.83 0.74 254
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korean 0.94 0.58 0.72 242
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thai 0.71 0.82 0.76 227
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accuracy 0.74 1199
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macro avg 0.76 0.74 0.74 1199
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weighted avg 0.76 0.74 0.74 1199
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```
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✅ Jifunze kuhusu [K-Neighbors](https://scikit-learn.org/stable/modules/neighbors.html#neighbors)
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## Kihesabu cha Support Vector
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Kihesabu cha Support-Vector ni sehemu ya familia ya [Support-Vector Machine](https://wikipedia.org/wiki/Support-vector_machine) ya mbinu za ML ambazo hutumika kwa kazi za uainishaji na regression. SVMs "huweka mifano ya mafunzo kwenye pointi katika nafasi" ili kuongeza umbali kati ya kategoria mbili. Data inayofuata huwekwa katika nafasi hii ili kategoria yao iweze kutabiriwa.
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### Zoezi - tumia Support Vector Classifier
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Hebu jaribu kupata usahihi bora zaidi kwa kutumia Support Vector Classifier.
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1. Ongeza koma baada ya kipengee cha K-Neighbors, na kisha ongeza mstari huu:
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```python
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'SVC': SVC(),
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```
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Matokeo ni mazuri sana!
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```output
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Accuracy (train) for SVC: 83.2%
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precision recall f1-score support
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chinese 0.79 0.74 0.76 242
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indian 0.88 0.90 0.89 234
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japanese 0.87 0.81 0.84 254
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korean 0.91 0.82 0.86 242
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thai 0.74 0.90 0.81 227
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accuracy 0.83 1199
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macro avg 0.84 0.83 0.83 1199
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weighted avg 0.84 0.83 0.83 1199
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```
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✅ Jifunze kuhusu [Support-Vectors](https://scikit-learn.org/stable/modules/svm.html#svm)
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## Vihesabu vya Ensemble
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Hebu fuata njia hadi mwisho, ingawa jaribio la awali lilikuwa zuri sana. Hebu jaribu 'Ensemble Classifiers, haswa Random Forest na AdaBoost:
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```python
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'RFST': RandomForestClassifier(n_estimators=100),
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'ADA': AdaBoostClassifier(n_estimators=100)
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```
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Matokeo ni mazuri sana, hasa kwa Random Forest:
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```output
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Accuracy (train) for RFST: 84.5%
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precision recall f1-score support
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chinese 0.80 0.77 0.78 242
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indian 0.89 0.92 0.90 234
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japanese 0.86 0.84 0.85 254
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korean 0.88 0.83 0.85 242
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thai 0.80 0.87 0.83 227
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accuracy 0.84 1199
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macro avg 0.85 0.85 0.84 1199
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weighted avg 0.85 0.84 0.84 1199
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Accuracy (train) for ADA: 72.4%
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precision recall f1-score support
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chinese 0.64 0.49 0.56 242
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indian 0.91 0.83 0.87 234
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japanese 0.68 0.69 0.69 254
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korean 0.73 0.79 0.76 242
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thai 0.67 0.83 0.74 227
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accuracy 0.72 1199
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macro avg 0.73 0.73 0.72 1199
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weighted avg 0.73 0.72 0.72 1199
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```
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✅ Jifunze kuhusu [Ensemble Classifiers](https://scikit-learn.org/stable/modules/ensemble.html)
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Njia hii ya Kujifunza Mashine "inaunganisha utabiri wa wahesabuji kadhaa wa msingi" ili kuboresha ubora wa modeli. Katika mfano wetu, tulitumia Random Trees na AdaBoost.
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- [Random Forest](https://scikit-learn.org/stable/modules/ensemble.html#forest), njia ya wastani, inajenga 'msitu' wa 'miti ya maamuzi' iliyo na nasibu ili kuepuka kufaa kupita kiasi. Kigezo cha n_estimators kimewekwa kwa idadi ya miti.
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- [AdaBoost](https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html) inafaa kihesabu kwa dataset na kisha inafaa nakala za kihesabu hicho kwa dataset hiyo hiyo. Inazingatia uzito wa vitu vilivyoainishwa vibaya na kurekebisha kifaa kwa kihesabu kinachofuata ili kusahihisha.
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---
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## 🚀Changamoto
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Kila moja ya mbinu hizi ina idadi kubwa ya vigezo unavyoweza kurekebisha. Tafiti vigezo vya default vya kila moja na fikiria kuhusu nini kurekebisha vigezo hivi kungeleta kwa ubora wa modeli.
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## [Jaribio la baada ya somo](https://gray-sand-07a10f403.1.azurestaticapps.net/quiz/24/)
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## Mapitio & Kujisomea
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Kuna maneno mengi ya kitaalamu katika masomo haya, kwa hivyo chukua muda kidogo kupitia [orodha hii](https://docs.microsoft.com/dotnet/machine-learning/resources/glossary?WT.mc_id=academic-77952-leestott) ya maneno muhimu!
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## Kazi
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[Cheza na vigezo](assignment.md)
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**Kanusho**:
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Hati hii imetafsiriwa kwa kutumia huduma za tafsiri za AI zinazotumia mashine. Ingawa tunajitahidi kwa usahihi, tafadhali fahamu kuwa tafsiri za kiotomatiki zinaweza kuwa na makosa au kutokamilika. Hati ya asili katika lugha yake ya asili inapaswa kuzingatiwa kama chanzo cha mamlaka. Kwa taarifa muhimu, tafsiri ya kitaalamu ya binadamu inapendekezwa. Hatutawajibika kwa kutoelewana au kutafsiri vibaya kunakotokana na matumizi ya tafsiri hii. |