Scikit-Learn Cheatsheet

Scikit-learn is a library in Python that provides many unsupervised and supervised learning algorithms. It’s built upon NumPy, pandas, and Matplotlib!

Linear regression in Scikit-Learn

Import and create the model

from sklearn.linear_model import LinearRegression your_model = LinearRegression()

Fit:

your_model.fit(x_training_data, y_training_data)

Predict:

predictions = your_model.predict(your_x_data)

.score(): returns the coefficient of determination R²

Naive Bayes classification in Scikit-Learn

Import and create the model:

from sklearn.naive_bayes import MultinomialNB your_model = MultinomialNB()

Fit:

your_model.fit(x_training_data, y_training_data)

Predict:

Returns a list of predicted classes - one prediction for every data point

predictions = your_model.predict(your_x_data)

For every data point, returns a list of probabilities of each class

probabilities = your_model.predict_proba(your_x_data)

K-nearest neighbors (KNN) in Scikit-Learn

Import and create the model:

from sklearn.neighbors import KNeighborsClassifier your_model = KNeighborsClassifier()

Fit:

your_model.fit(x_training_data, y_training_data)

Predict:

Returns a list of predicted classes - one prediction for every data point

predictions = your_model.predict(your_x_data)

For every data point, returns a list of probabilities of each class

probabilities = your_model.predict_proba(your_x_data)

K-means clustering in Scikit-Learn

Import and create the model:

from sklearn.cluster import KMeans your_model = KMeans(n_clusters=4, init='random')

n_clusters: number of clusters to form and number of centroids to generate init: method for initialization k-means++: K-Means++ [default] random: K-Means random_state: the seed used by the random number generator [optional]

Fit:

your_model.fit(x_training_data)

Predict:

predictions = your_model.predict(your_x_data)

Validating a machine learning model

Import and print accuracy, recall, precision, and F1 score:

from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score

print(accuracy_score(true_labels, guesses)) print(recall_score(true_labels, guesses)) print(precision_score(true_labels, guesses)) print(f1_score(true_labels, guesses))

Import and print the confusion matrix:

from sklearn.metrics import confusion_matrix

print(confusion_matrix(true_labels, guesses))

Splitting data into training and test sets

from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.8, test_size=0.2)

train_size: the proportion of the dataset to include in the train split test_size: the proportion of the dataset to include in the test split random_state: the seed used by the random number generator [optional]