Scikit-Learn Cheatsheet

Scikit-learn is a library in Python that provides many unsupervised and supervised learning algorithms. It’s built upon some of the technology you might already be familiar with, like NumPy, pandas, and Matplotlib!

As you build robust Machine Learning programs, it’s helpful to have all the sklearn commands all in one place in case you forget.

Linear Regression

Import and create the model:

from sklearn.linear_model import LinearRegression

your_model = LinearRegression()

Fit:

your_model.fit(x_training_data, y_training_data)

• .coef_: contains the coefficients
• .intercept_: contains the intercept

Predict:

predictions = your_model.predict(your_x_data)

• .score(): returns the coefficient of determination R²

Naive Bayes

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

Import and create the model:

from sklearn.neigbors 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

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 the 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))

Training Sets 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]

Happy Coding!