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)

Predict:

predictions = your_model.predict(your_x_data)

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()

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 and Test Sets

from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=proportion_for_test_set)
Made in NYC © 2018 Codecademy