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 generateinit: method for initializationk-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 splittest_size: the proportion of the dataset to include in the test splitrandom_state: the seed used by the random number generator [optional]