Scikit-Learn Cheat Sheet (2022), Python for Data Science

[Maksim-Sheyngalts]

Scikit-Learn Cheat Sheet (2022), Python for Data Science

The absolute basics for beginners learning Scikit-Learn in 2022



Photo from Unsplash by Tim Stief

Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression, clustering algorithms, and efficient tools for data mining and data analysis. It’s built on NumPy, SciPy, and Matplotlib.

Sections: 1. Basic Example 2. Loading the Data 3. Training and Test Data 4. Preprocessing the Data 5. Create Your Model 6. Model Fitting 7. Prediction 8. Evaluate Your Model’s Performance 9. Tune Your Model

Basic Example:

The code below demonstrates the basic steps of using scikit-learn to create and run a model on a set of data.

The steps in the code include: loading the data, splitting into train and test sets, scaling the sets, creating the model, fitting the model on the data, using the trained model to make predictions on the test set, and finally evaluating the performance of the model.

from sklearn import neighbors, datasets, preprocessing from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score iris = datasets.load_iris() X,y = iris.data[:,:2], iris.target X_train, X_test, y_train, y_test = train_test_split(X,y) scaler = preprocessing_StandardScaler().fit(X_train) X_train = scaler.transform(X_train) X_test = scaler.transform(X_test) knn = neighbors.KNeighborsClassifier(n_neighbors = 5) knn.fit(X_train, y_train) y_pred = knn.predict(X_test) accuracy_score(y_test, y_pred)

Loading the Data

Your data needs to be numeric and stored as NumPy arrays or SciPy spare matrix. Other types that convert to numeric arrays, such as Pandas DataFrame’s are also acceptable.

import numpy as np X = np.random.random((10,5))array([[0.21069686, 0.33457064], [0.23887117, 0.6093155 ], [0.48848537, 0.62649292]])>>> y = np.array(['A','B','A'])array(['A', 'B', 'A'])

Training and Test Data

Splits the dataset into training and test sets for both the X and y variables.

from sklearn.model_selection import train_test_split X_train,X_test,y_train,y_test = train_test_split(X,y, random_state = 0)

Preprocessing The Data

Getting the data ready before the model is fitted.

Standardization

Standardize’s the features by removing the mean and scaling to unit variance.

from sklearn.preprocessing import StandardScaler scaler = StandardScaler().fit(X_train) standarized_X = scaler.transform(X_train) standarized_X_test = scaler.transform(X_test)

Normalization

Each sample (i.e. each row of the data matrix) with at least one non-zero component is rescaled independently of other samples so that its norm equals one.

from sklearn.preprocessing import Normalizer scaler = Normalizer().fit(X_train) normalized_X = scaler.transform(X_train) normalized_X_test = scaler.transform(X_test)

Binarization

Binarize data (set feature values to 0 or 1) according to a threshold.

from sklearn.preprocessing import Binarizer binarizer = Binarizer(threshold = 0.0).fit(X) binary_X = binarizer.transform(X_test)

Encoding Categorical Features

Encode’s target labels with values between 0 and n_classes-1.

from sklearn import preprocessing le = preprocessing.LabelEncoder() le.fit_transform(X_train)

Imputing Missing Values

Imputation transformer for completing missing values.

from sklearn.impute import SimpleImputer imp = SimpleImputer(missing_values = 0, strategy = 'mean') imp.fit_transform(X_train)

Generating Polynomial Features

Generate a new feature matrix consisting of all polynomial combinations of the features with degrees less than or equal to the specified degree.

from sklearn.preprocessing import PolynomialFeatures poly = PolynomialFeatures(5) poly.fit_transform(X)

Create Your Model

Creation of various supervised and unsupervised learning models.

Supervised Learning Models

Linear Regression

from sklearn.linear_model import LinearRegression lr = LinearRegression(normalize = True)

Support Vector Machines (SVM)

from sklearn.svm import SVC svc = SVC(kernel = 'linear')

Naive Bayes

from sklearn.naive_bayes import GaussianNB gnb = GaussianNB()

KNN

from sklearn import neighbors knn = neighbors.KNeighborsClassifier(n_neighbors = 5)

Unsupervised Learning Models

Principal Component Analysis (PCA)

from sklearn.decomposition import PCA pca = PCA(n_components = 0.95)

K means

from sklearn.cluster import KMeans k_means = KMeans(n_clusters = 3, random_state = 0)

Model Fitting

Fitting supervised and unsupervised learning models onto data.

Supervised learning

Fit the model to the data

lr.fit(X, y) knn.fit(X_train,y_train) svc.fit(X_train,y_train)

Unsupervised learning

Fit the model to the data

k_means.fit(X_train)

Fit to data, then transform it

pca_model = pca.fit_transform(X_train)

Prediction

Predicting test sets using trained models.

Predict labels

Supervised Estimators

y_pred = lr.predict(X_test)#Unsupervised Estimators y_pred = k_means.predict(X_test)

Estimate probability of a label

y_pred = knn.predict_proba(X_test)

Evaluate Your Model’s Performance

Various regression and classification metrics that determine how well a model performed on a test set.

Classification Metrics

Accuracy Score

knn.score(X_test,y_test) from sklearn.metrics import accuracy_score accuracy_score(y_test,y_pred)

Classification Report

from sklearn.metrics import classification_report print(classification_report(y_test,y_pred))

Confusion Matrix

from sklearn .metrics import confusion_matrix print(confusion_matrix(y_test,y_pred))

Regression Metrics

Mean Absolute Error

from sklearn.metrics import mean_absolute_error mean_absolute_error(y_test,y_pred)

Mean Squared Error

from sklearn.metrics import mean_squared_error mean_squared_error(y_test,y_pred)

R² Score

from sklearn.metrics import r2_score r2_score(y_test, y_pred)

Clustering Metrics

Adjusted Rand Index

from sklearn.metrics import adjusted_rand_score adjusted_rand_score(y_test,y_pred)

Homogeneity

from sklearn.metrics import homogeneity_score homogeneity_score(y_test,y_pred)

V-measure

from sklearn.metrics import v_measure_score v_measure_score(y_test,y_pred)

Cross-Validation

Evaluate a score by cross-validation

from sklearn.model_selection import cross_val_score print(cross_val_score(knn, X_train, y_train, cv=4))

Tune Your Model

Finding correct parameter values that will maximize a model’s prediction accuracy.

Grid Search

Exhaustive search over specified parameter values for an estimator. The example below attempts to find the right amount of clusters to specify for knn to maximize the model’s accuracy.

from sklearn.model_selection import GridSearchCV params = {'n_neighbors': np.arange(1,3), 'metric':['euclidean','cityblock']} grid = GridSearchCV(estimator = knn, param_grid = params) grid.fit(X_train, y_train) print(grid.bestscore) print(grid.bestestimator.n_neighbors)

Randomized Parameter Optimization

Randomized search on hyperparameters. In contrast to Grid Search, not all parameter values are tried out, but rather a fixed number of parameter settings is sampled from the specified distributions. The number of parameter settings that are tried is given by n_iter.

from sklearn.model_selection import RandomizedSearchCV params = {'n_neighbors':range(1,5), 'weights':['uniform','distance']} rsearch = RandomizedSearchCV(estimator = knn, param_distributions = params, cv = 4, n_iter = 8, random_state = 5) rseach.fit(X_train, y_train) print(rsearch.bestscore)

Scikit-learn is an extremely useful library for various machine learning models. The sections above provided a basic step-by-step process to perform an analysis on different models. If you want to learn more however check out the documentation for Scikit-Learn, as there are still plenty of useful functions you could learn.

[Maksim-Sheyngalts]

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