ML-For-Beginners/translations/pcm/4-Classification/2-Classifiers-1/README.md

Cuisine classifiers 1

For dis lesson, you go use di dataset wey you save from di last lesson wey full wit balanced, clean data wey dey all about cuisines.

You go use dis dataset wit plenty classifiers dem to predict one kain national cuisine based on one group of ingredients. While you dey do am, you go learn more about some of di ways wey algorithms fit take help for classification tasks.

Pre-lecture quiz

Preparation

Assuming say you don finish Lesson 1, make sure say one cleaned_cuisines.csv file dey for di root /data folder for dis four lessons.

Exercise - predict one national cuisine

1. For dis lesson's notebook.ipynb folder, import dat file plus di Pandas library:

   import pandas as pd
   cuisines_df = pd.read_csv("../data/cleaned_cuisines.csv")
   cuisines_df.head()
   

   Di data look like dis:

	Unnamed: 0	cuisine	almond	angelica	anise	anise_seed	apple	apple_brandy	apricot	armagnac	...	whiskey	white_bread	white_wine	whole_grain_wheat_flour	wine	wood	yam	yeast	yogurt	zucchini
0	0	indian	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
1	1	indian	1	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
2	2	indian	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
3	3	indian	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
4	4	indian	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	1	0

1. Now, import more libraries:

   from sklearn.linear_model import LogisticRegression
   from sklearn.model_selection import train_test_split, cross_val_score
   from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve
   from sklearn.svm import SVC
   import numpy as np
   

2. Divide X and y coordinates into two dataframes for training. cuisine fit be labels dataframe:

   cuisines_label_df = cuisines_df['cuisine']
   cuisines_label_df.head()
   

   E go look like dis:

   0    indian
   1    indian
   2    indian
   3    indian
   4    indian
   Name: cuisine, dtype: object
   

3. Drop dat Unnamed: 0 column and di cuisine column by using drop(). Save di rest data as trainable features:

   cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)
   cuisines_feature_df.head()
   

   Your features go look like dis:

	almond	angelica	anise	anise_seed	apple	apple_brandy	apricot	armagnac	artemisia	artichoke	...	whiskey	white_bread	white_wine	whole_grain_wheat_flour	wine	wood	yam	yeast	yogurt	zucchini
0	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
1	1	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
2	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
3	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	0	0
4	0	0	0	0	0	0	0	0	0	0	...	0	0	0	0	0	0	0	0	1	0

Now you ready to train your model!

Choosing your classifier

Now wey your data clean and don ready for training, you gats make decision which algorithm you go use for di work.

Scikit-learn put classification under Supervised Learning, and for dat category you go find plenty ways to classify. Di variety dey confuse small for first look. Di methods wey follow dey include classification techniques:

• Linear Models
• Support Vector Machines
• Stochastic Gradient Descent
• Nearest Neighbors
• Gaussian Processes
• Decision Trees
• Ensemble methods (voting Classifier)
• Multiclass and multioutput algorithms (multiclass and multilabel classification, multiclass-multioutput classification)

You fit also use neural networks to classify data, but dat one no dey inside dis lesson scope.

Which classifier to choose?

So, which classifier you go choose? Most times, you fit run several classifiers and see wey one get better result. Scikit-learn get side-by-side comparison for one created dataset, wey compare KNeighbors, SVC two ways, GaussianProcessClassifier, DecisionTreeClassifier, RandomForestClassifier, MLPClassifier, AdaBoostClassifier, GaussianNB and QuadraticDiscrinationAnalysis, and dem show di results as visual:

Charts wey Scikit-learn documentation generate

AutoML fit solve dis kind wahala well well by to run these comparisons for cloud, so dat you fit choose di best algorithm for your data. Try am here

Better approach

Better pass just guess work na to follow di ideas wey dey inside dis downloadable ML Cheat sheet. For here, we see sey for our multiclass problem, we get some choices:

One part of Microsoft Algorithm Cheat Sheet, wey show multiclass classification options

✅ Download dis cheat sheet, print am come hang am for wall!

Reasoning

Make we try reason our way through different approaches based on wetin we get:

• Neural networks too heavy. Based on our clean but small dataset, plus the fact say we dey run training local for notebooks, neural networks too heavy for dis task.
• No two-class classifier. We no dey use two-class classifier, so we no go use one-vs-all.
• Decision tree or logistic regression fit work. Decision tree fit work, or logistic regression for multiclass data.
• Multiclass Boosted Decision Trees solve other kinds problem. Dis multiclass boosted decision tree best for nonparametric tasks, e.g. tasks wey dey build rankings, so e no concern us.

Using Scikit-learn

We go use Scikit-learn analyze our data. But, many ways dey to use logistic regression for Scikit-learn. Look di parameters wey you fit pass.

Basically, two parameters matter - multi_class and solver - we need to specify when we ask Scikit-learn to do logistic regression. Di value of multi_class determine how e go behave. Di solver na wetin algorithm to use be dat. No all solvers fit with all multi_class values.

According to docs, for multiclass case, di training algorithm:

• Use one-vs-rest (OvR) scheme, if multi_class option set to ovr
• Use cross-entropy loss, if multi_class option set to multinomial. (Currently multinomial option dey supported only by ‘lbfgs’, ‘sag’, ‘saga’ and ‘newton-cg’ solvers.)"

🎓 Di 'scheme' fit be 'ovr' (one-vs-rest) or 'multinomial'. Because logistic regression na to support binary classification, these schemes help am better handle multiclass classification tasks. source

🎓 Di 'solver' na "di algorithm to use inside di optimization problem". source.

Scikit-learn get table to explain how solvers dey handle different challenges from many kinds data structures:

Exercise - split di data

We fit focus on logistic regression for our first training because you just learn am for previous lesson. Split your data into training and testing groups by calling train_test_split():

X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)

Exercise - use logistic regression

Since you dey use multiclass case, you gats choose which scheme to use and which solver to set. Use LogisticRegression with multiclass setting and liblinear solver for train.

1. Create logistic regression wit multi_class set to ovr and solver set to liblinear:

   lr = LogisticRegression(multi_class='ovr',solver='liblinear')
   model = lr.fit(X_train, np.ravel(y_train))
   
   accuracy = model.score(X_test, y_test)
   print ("Accuracy is {}".format(accuracy))
   

   ✅ Try different solver like lbfgs, wey dem dey use as default many times

   Note, use Pandas ravel function to flatten your data when e needed.

   Di accuracy good pass 80%!

2. You fit see dis model dey work by testing one row of data (#50):

   print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}')
   print(f'cuisine: {y_test.iloc[50]}')
   

   Di result go print:

   ingredients: Index(['cilantro', 'onion', 'pea', 'potato', 'tomato', 'vegetable_oil'], dtype='object')
   cuisine: indian
   

   ✅ Try different row number come check di results

3. Diggin deep, you fit check the accuracy of dis prediction:

   test= X_test.iloc[50].values.reshape(-1, 1).T
   proba = model.predict_proba(test)
   classes = model.classes_
   resultdf = pd.DataFrame(data=proba, columns=classes)
   
   topPrediction = resultdf.T.sort_values(by=[0], ascending = [False])
   topPrediction.head()
   

   Di result e print - Indian cuisine na di best guess, wit good probability:

   	0
   indian	0.715851
   chinese	0.229475
   japanese	0.029763
   korean	0.017277
   thai	0.007634

   ✅ You fit explain why di model sure sey na Indian cuisine dis be?

4. Get more detail by printing classification report, like how you do for regression lessons:

   y_pred = model.predict(X_test)
   print(classification_report(y_test,y_pred))
   

   	precision	recall	f1-score	support
   chinese	0.73	0.71	0.72	229
   indian	0.91	0.93	0.92	254
   japanese	0.70	0.75	0.72	220
   korean	0.86	0.76	0.81	242
   thai	0.79	0.85	0.82	254
   accuracy			0.80	1199
   macro avg	0.80	0.80	0.80	1199
   weighted avg	0.80	0.80	0.80	1199

🚀Challenge

For dis lesson, you use your clean data to build machine learning model wey fit predict national cuisine based on series of ingredients dem. Take some time read through all di options wey Scikit-learn provide to classify data. Dig deep inside di concept of 'solver' to understand wetin dey happen behind di scenes.

Post-lecture quiz

Review & Self Study

Dig small more inside di math behind logistic regression inside this lesson

Assignment

Study the solvers

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Disclaimer: Dis document dem don translate am wit AI translation service Co-op Translator. Even tho we dey try make am correct, abeg sabi say automated translations fit get errors or mistakes. Di original document for im own language na di correct source. For important matter, e better make person wey sabi do professional translation handle am. We no go take responsibility for any misunderstanding or wrong meaning wey fit show from dis translation.