Scikit-learn (Sklearn) is the most robust machine learning library in Python. It uses a Python consistency interface to provide a set of efficient tools for statistical modeling and machine learning, like classification, regression, clustering, and dimensionality reduction. NumPy, SciPy, and Matplotlib are the foundations of this package, primarily written in Python.

Machine learning academics and data scientists have flocked to the scikit-learn Python package in the last five years. It includes a collection of tools for tuning model hyperparameters, evaluating, and chaining (pipelines), as well as a unified interface for using models and training. 

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Julia now has these features thanks to ScikitLearn.jl. Sci-kit’s main purpose is to bring Python-defined models and Julia into the unified “sci-kit learn” framework. This library makes considerable use of docstrings (function_name at the REPL). In this article, we will explore what Sklearn Regression Models are.

What Are Sklearn Regression Models?

Before moving on to the Sklearn Models, let us first see what Machine Learning is.

Machine Learning is the process of teaching a computer to learn and implement tasks without having to write them down explicitly. This indicates that the system is capable of making decisions to some extent. Three types of Machine Learning Models can be implemented using the Sklearn Regression Models:

  • Reinforced Learning
  • Unsupervised Learning
  • Supervised Learning

Before we dive deeper into these categories, let us look at the most popular Regression Methods in Sklearn to implement them.

Gradient Boosting Regression


from sklearn.ensemble import GradientBoostingRegressor

Elastic Net Regression


from sklearn.linear_model import ElasticNet

Stochastic Gradient Descent Regression


from sklearn.linear_model import SGDRegressor

Support Vector Machine


from sklearn.svm import SVR

Bayesian Ridge Regression


from sklearn.linear_model import BayesianRidge

CatBoost Regressor


from catboost import CatBoostRegressor

Kernel Ridge Regression


from sklearn.kernel_ridge import KernelRidge

Linear Regression


from sklearn.linear_model import LinearRegression

XGBoost Regressor


from xgboost.sklearn import XGBRegressor

LGBM Regressor


from lightgbm import LGBMRegressor

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Three Major Categories of Sklearn Regression Models

Now that we’ve gone through the Regression Methods in Sklearn, let us explore the three major categories of Sklearn Regression Models.

Supervised Learning

The system learns under the supervision of a teacher in this machine learning paradigm. For training, the model has a known input and output. The instructor is aware of the outcome during the training process and trains the model to reduce prediction error. Classification and regression are the two primary types of supervised learning algorithms.

This is sub-categorized into:

  • Classification: The outcome of classification is discrete data. To put it more simply, we will categorize data based on particular characteristics. Differentiating between apples and oranges, for example, is based on their shapes, colors, textures, and other characteristics. Color and texture are features in this sample form, and the output is "Apple" or "Orange," which are Classes. The process is termed Classification since the output is known as classes.
  • Regression: The output of regression is continuous data. We anticipate the trends of training data using this strategy, which is based on characteristics. The output is numeric, a real number, but it does not belong to any particular category or class. Predicting property prices, for example, is based on characteristics such as the size of the house, its location, and the number of stores, among others.

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Unsupervised Learning

A model in which the learning process is conducted without the presence of a supervisor is known as unsupervised learning. The model only accepts input for training, and only the inputs are used to generate the output. Clustering is the most common type of unsupervised learning, in which we group similar items to uncover patterns in unlabeled datasets.

Reinforced Learning

Reinforcement Learning refers to models that learn to make decisions based on incentives or penalties, intending to maximize rewards by providing the right answers. Reinforcement learning is a method of learning in which a robot learns by executing tasks and receiving feedback. It is extensively employed in gaming algorithms and robotics.

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In this article, we discussed SkLearn and its types of Regression Models. There is a lot more to learn and understand apart from what is in this article. 

To dive deeper into Sklearn and the various Machine learning models, check out Simplilearn's Data Science Bootcamp. This program features Caltech faculty and IBM experts’ masterclasses, exclusive hackathons, and an intensive Q&A session by IBM and so much more. Explore now!

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