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 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]
Happy Coding!