Learn About Core Features Of Scikit-learn

In the realm of artificial intelligence (AI), scikit-learn is a prominent open-source and machine learning (ML) library. Classification, regression, clustering, and dimensionality reduction are just a few of the useful tools in the scikit-learn toolkit for ML and statistical modelling. In this tutorial, you will learn about scikit-learn features.

Scikit-learn is primarily written in Python, while several fundamental algorithms are written in programming language Cython to increase efficiency. Scikit-learn also works with a variety of other Python libraries, including graphing libraries Matplotlib, pandas DataFrames, NumPy for array vectorization, Plotly, Scipy, etc.

Scikit-learn Library” has many important features, and some of them are listed below:

  • Supervised Models: A training set is used in supervised learning to teach models to produce the desired output. This training dataset contains both correct and incorrect outputs, allowing the model to learn over time. The loss function is used by the algorithm to measure its accuracy, and it adjusts until the error is sufficiently minimized. Example: Linear regression, random forest, XGBoost, etc.
  • Datasets: Scikit-learn includes a few small standard datasets that do not require the download of any files from a third-party website. Example: load_iris()  and load_diabetes() are some in-built datasets to practice on.
  • Parameter Tuning: Hyperparameter optimization is the process of conducting a search to find the set of specific model configuration parameters that result in the model’s optimal performance on a certain dataset.
  • Feature Selection: Feature selection is a technique for reducing variables by using specific criteria to select the variables that are most useful in the dataset which can help to predict the target in the model. Example: “VarianceThreshold” feature selection, univariate feature selection with SelectKBest, recursive feature elimination (RFE) and feature selection sequential feature selection (SFS).
  • Dimensionality Reduction: Dimensionality reduction is an unsupervised machine learning strategy that selects a collection of important features to reduce the number of feature variables for each data sample. Example: Backward Feature Elimination, principal component analysis, etc.

Feature selection is similar to dimensionality reduction in that the goal is to reduce the number of features, but both are fundamentally different. The distinction is that feature selection allows you to decide which features to keep or delete from the dataset. Dimensionality reduction, on the other hand, leads to the projection of data that ends in new input features.

  • Cross-validation: Cross-validation is a method of testing ML models that involves the training various models on subsets of the available input data and then assessing them on the complementary subset. Overfitting, or the failure to generalize a pattern, can be detected using cross-validation.
  • Ensemble Methods: Ensemble methods is basically a technique for developing multiple models and then combining the latter to get better results. Usually, ensemble methods produces more accurate results. Example: random forest, AdaBoost, GBM, etc.
  • Feature Extraction: The feature extraction module can be used to extract features in a format that machine learning algorithms can understand from datasets that include formats such as text and images.
  • Clustering: Cluster analysis, or clustering, is a sort of unsupervised machine learning paradigm. It discovers natural grouping in data automatically. Unlike supervised learning (such as predictive modelling), clustering algorithms just evaluate the incoming data and look for natural groups or clusters in feature space. Example: k-means clustering.

(I prefer using scikit-learn since it provides a lot of versatility. The official documentation includes many examples. In the next half of this article, I’ll show you some of the scikit-learn library’s more impressive features that you may not be aware of.)

1.   Plot The Decision Tree

The “Plot Tree Function” can be used to illustrate a decision tree model. Plot function lets you add feature names with a parameter, “feature_names”.

from sklearn.tree import plot_tree

2.   Dummy Features

If you want to generate dummy features in a dataset with a particular value, you can do that by using the “Add Dummy Feature” built-in in the scikit-learn library.

from sklearn.preprocessing import add_dummy_feature

3.   Impute Missing Values with Iterative Imputer

To replace (impute) missing values in datasets, we usually employ straightforward methods. For numerical features, these methods are mean/median, and for categorical features, mode is one of the methods that can be used. Advanced approaches such as IterativeImputer are also available. IterativeImputer employs a machine learning model like BayesianRidge to estimate missing values based on all attributes in your dataset. This indicates that the dependent variable will be the feature with missing values, while the other characteristics will be independent variables.

from sklearn.impute import IterativeImputer

4.   Identify Estimators As Regressors/ Classifiers

With two simple functions in the scikit-learn library, you can tell if a model solves a regression/ classification task. “is_classifier” and “is_regressor” are two functions that can be used to determine whether something is a classifier or a regressor.

from sklearn.base import is_regressor
from sklearn.base import is_classifier

5.   Cross-Validation And Prediction

You may use scikit-cross learn’s val predict function to perform cross-validation and prediction for the estimator.

from sklearn.model_selection import cross_val_predict

6.   Pick Important Features Using SelectFromModel

When running a model, all the features are not important. You may use the “SelectFromModel Function” to find and pick important features for your model. SelectFromModel just drops less crucial features based on a specified threshold, that is why it is less resilient.

from sklearn.feature_selection import SelectFromModel

7.   RandomizedSearchCV (hyperparameter tuning)

The function RandomizedSearchCV trains and evaluates multiple models by selecting a random number of hyperparameter distributions from a predefined list. After training numerous versions of the model with randomly chosen combinations of a hyperparameter, the function selects the most successful version with the best set of parameter values.

from sklearn.model_selection import RandomizedSearchCV

8.   Load Text Files

You can use the load files function in scikit-learn to load text files. Every folder within the main/root folder will be treated as a separate category by the load files, and all docs within the same folder will be assigned to that specific category.

from sklearn.datasets import load_files

9. Determine Target Data Type

We have independent variables and the targeted variable when working with supervised ML Mode. To decide whether to solve a problem using regression or classification, we need to know what type of data is the target variable (Y). The target variable’s type of data can be determined using the function type of target.

from sklearn.utils.multiclass import type_of_target


Scikit-learn is one of the most popular ML libraries. It has all the features which can be used to create an end-to-end ML solution. You may also utilize scikit-learn in your machine learning project and apply some of its lesser known capabilities as explained in this article.


Official documentation for: