With the recent advancement in AI, scikit-learn has become the most popular and essential tools for data scientists and machine learning engineers to develop resilient machine learning models.
Scikit-learn offers a range of algorithms for supervised, unsupervised and reinforcement learning algorithms which include non-linear, linear, ensemble, association, clustering, dimension reduction model and so much more.
It also provides evaluation, scaling, and selection tools to ensure you select the best models for a given objective.
How to install scikit-learn
To install scikit-learn, you can use PyPI or conda
Ps: this installation works for all platform(Windows, MacOS, linux)
To install scikit-learn via PyPI, open your terminal and run the command below
$ pip install -U scikit-learn
To install scikit-learn via conda use the command below.
$ conda create -n sklearn-env -c conda-forge scikit-learn $ Conda activate sklearn-env
Scikit learn example
Let’s create a simple linear regression model with scikit-learn
from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split import numpy as np # Generate some data for the example np.random.seed(0) X = np.random.rand(1000, 1) y = 9 + 6 * X + np.random.rand(1000, 1) # Split the data into training and test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3) # Create a LinearRegression object and fit it to the training data reg = LinearRegression() reg.fit(X_train, y_train) # Make predictions on the test set y_pred = reg.predict(X_test) # Print the coefficient and intercept of the fitted model print("Coefficient: ", reg.coef_) print("Intercept: ", reg.intercept_)
This code above uses synthetics data, perform cross validation, and fit the model to linear regression then compute for coefficient and the intercept of the model.
Scikit-learn in action: