Scikit-Learn Cheatsheet
Scikit-Learn Cheatsheet
Scikit-Learn Cheatsheet
Scikit-learnis a library in Python that provides many unsupervised andsupervised learningalgorithms. It’s built upon some of the technology you might already be familiar with, likeNumPy, pandas, and Matplotlib!
As you build robust Machine Learning programs, it’s helpful to have all thesklearncommands all in one place in case you forget.
Linear regression in Scikit-Learn
Import and create the model:
from sklearn.linear_model import LinearRegression
your_model = LinearRegression()
Copy to clipboardfrom sklearn.linear_model import LinearRegression
your_model = LinearRegression()Fit:
your_model.fit(x_training_data, y_training_data)
Copy to clipboardyour_model.fit(x_training_data, y_training_data)- .coef_: contains the coefficients
- .intercept_: contains the intercept
Predict:
predictions = your_model.predict(your_x_data)
Copy to clipboardpredictions = your_model.predict(your_x_data)- .score(): returns the coefficient of determination R²
Naive Bayes classification in Scikit-Learn
from sklearn.naive_bayes import MultinomialNB
your_model = MultinomialNB()
Copy to clipboardfrom sklearn.naive_bayes import MultinomialNB
your_model = MultinomialNB()your_model.fit(x_training_data, y_training_data)
Copy to clipboard# Returns a list of predicted classes - one prediction for every data pointpredictions = your_model.predict(your_x_data)
# For every data point, returns a list of probabilities of each classprobabilities = your_model.predict_proba(your_x_data)
Copy to clipboard# Returns a list of predicted classes - one prediction for every data pointpredictions = your_model.predict(your_x_data)
# For every data point, returns a list of probabilities of each classprobabilities = your_model.predict_proba(your_x_data)K-nearest neighbors (KNN) in Scikit-Learn
from sklearn.neighbors import KNeighborsClassifier
your_model = KNeighborsClassifier()
Copy to clipboardfrom sklearn.neighbors import KNeighborsClassifier
your_model = KNeighborsClassifier()your_model.fit(x_training_data, y_training_data)
Copy to clipboard# Returns a list of predicted classes - one prediction for every data pointpredictions = your_model.predict(your_x_data)
# For every data point, returns a list of probabilities of each classprobabilities = your_model.predict_proba(your_x_data)
Copy to clipboardK-means clustering in Scikit-Learn
from sklearn.cluster import KMeans
your_model = KMeans(n_clusters=4, init='random')
Copy to clipboardfrom 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 initializationk-means++: K-Means++ [default]random: K-Means
- random_state: the seed used by the random number generator [optional]
your_model.fit(x_training_data)
Copy to clipboardyour_model.fit(x_training_data)predictions = your_model.predict(your_x_data)
Copy to clipboardValidating a machine learning 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))
Copy to clipboardfrom 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))
Copy to clipboardfrom sklearn.metrics import confusion_matrix
print(confusion_matrix(true_labels, guesses))Splitting data into 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, train_size=0.8, test_size=0.2)
Copy to clipboardfrom 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]
Conclusion
Scikit-Learnprovides a powerful and user-friendly framework for implementing machine learning models in Python. From regression and classification to clustering and model validation, it simplifies complex tasks with efficient built-in functions. Whether you’re training models, making predictions, or evaluating performance, Scikit-Learn equips you with the tools needed to build robust machine learning applications.
If you’re interested in learning more about Scikit-Learn and its applications in machine learning, please check out ourAI Catalog of articles!
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