Scikit-learn Cheat Sheet by lkakashv

# 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

# 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)

# 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)

# 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)

# 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))

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]