Scikit-Learn Intro
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:
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 of the prediction.
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.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
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++ - selects initial cluster centers for k-mean clustering in a smart way to speed up convergence
random - choose k random observations for the initial centroids
random_state - the seed used by the random number generator
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))