Dimensionality reduction, an unsupervised machine learning method is used to reduce the number of feature variables for each data sample selecting set of principal features. Principal Component Analysis (PCA) is one of the popular algorithms for dimensionality reduction available in Sklearn. In this tutorial, we perform dimensionality reduction using principal component analysis and incremental principal component analysis using Python Scikit-learn (Sklearn). Using Principal Component Analysis (PCA) PCA is a statistical method that linearly project the data into new feature space by analyzing the features of original dataset. The main concept behind PCA is to select the “principal” characteristics of the ... Read More

Random projection is a dimensionality reduction and data visualization method to simplify the complexity of highly dimensional data. It is basically applied to the data where other dimensionality reduction techniques such as Principal Component Analysis (PCA) can not do the justice to data. Python Scikit-learn provides a module named sklearn.random_projection that implements a computationally efficient way to reduce the data dimensionality. It implements the following two types of an unstructured random matrix − Gaussian Random Matrix Sparse Random Matrix Implementing Gaussian Random Projection For implementing Gaussian random matrix, random_projection module uses GaussianRandomProjection() function which reduces the dimensionality by ... Read More

Naïve Bayes classification, based on the Bayes theorem of probability, is the process of predicting the category from unknown data sets. Scikit-learn has three Naïve Bayes models namely, Gaussian Naïve Bayes Bernoulli Naïve Bayes Multinomial Naïve Bayes In this tutorial, we will learn Gaussian Naïve Bayes and Bernoulli Naïve Bayes classifiers using Python Scikit-learn (Sklearn). Gaussian Naïve Bayes Classifier Gaussian naïve bayes classifier is based on a continuous distribution characterized by mean and variance. With the help of an example, let’s see how we can use the Scikit-Learn Python ML library to build a Gaussian Naïve Bayes classifier. ... Read More

Random forest is a supervised machine learning algorithm that is used for classification, regression, and other tasks by creating decision trees on data samples. After creating the decision trees, a random forest classifier collects the prediction from each of them and selects the best solution by means of voting. One of the best advantages of a random forest classifier is that it reduces overfitting by averaging the result. That is the reason we get better results as compared to a single decision tree. Steps to Create Random Forest Classifier We can follow the below steps to create a random forest ... Read More

With the help of the Scikit-learn python library, we can get the dictionary-like objects of a dataset. Some of the interesting attributes of dictionary-like objects are as follows − data − It represents the data to learn. target − It represents the regression target. DESCR − The description of the dataset. target_names − It gives the target names on of the dataset. feature_names − It gives the feature names from the dataset. Example 1 In the example below we use the California Housing dataset to get its dictionary-like objects. # Import necessary libraries import sklearn import pandas as ... Read More

Binarization is a preprocessing technique which is used when we need to convert the data into binary numbers i.e., when we need to binarize the data. The scikit-learn function named Sklearn.preprocessing.binarize() is used to binarize the data. This binarize function is having threshold parameter, the feature values below or equal this threshold value is replaced by 0 and value above it is replaced by 1. In this tutorial, we will learn to binarize data and sparse matrices using Scikit-learn (Sklearn) in Python. Example Let’s see an example in which we preprocess a numpy array into binary numbers − # Importing ... Read More

Python Scikit-learn provides us make_spd_matrix() function with the help of which we can generate a random symmetric positive-definite matrix. In this tutorial, we will generate symmetric positive-definite and sparse spd matrices using Scikit-learn (Sklearn) in Python. To do so, we can follow the below given steps − Step 1 − Import the libraries sklearn.datasets.make_spd_matrix, matplotlib, and seaborn which are necessary to execute the program. Step 2 − Create an object of make_spd_matrix() and provide the value of n_dim parameter which represents the matrix dimension. Step 3 − Use matplotlib lib to set the size of the output figure. Step 4 − Use seaborn ... Read More

Python Scikit-learn provides us make_regression() function with the help of which we can generate a random regression problem. In this tutorial, we will learn to generate random regression problems and random regression problems with sparse uncorrelated design. Random Regression Problem To generate a random regression problem using Python Scikit-learn, we can follow the below given steps − Step 1 − Import the libraries sklearn.datasets.make_regression and matplotlib which are necessary to execute the program. Step 2 − Provide the number of samples and other parameters. Step 3 − Use matplotlib library to set the size and style of the output figure. Step 4 − ... Read More

Scikit-learn provides us make_classification() function with the help of which we can plot randomly generated classification datasets with different numbers of informative features, clusters per class and classes. In this tutorial, we will learn how to generate and plot classification dataset using Python Scikit-learn. Dataset with One Informative Feature and One Cluster per Class To generate and plot classification dataset with one informative feature and one cluster, we can take the below given steps − Step 1 − Import the libraries sklearn.datasets.make_classification and matplotlib which are necessary to execute the program. Step 2 − Create data points namely X and y ... Read More

In this tutorial, we will learn how to generate an array with a constant block diagonal structure and with a block checkerboard structure for bi-clustering using Python Scikit-learn (Sklearn). Generating an Array with a Constant Block Diagonal Structure To generate an array with constant block diagonal structure for biclustering, we can take the following steps − Step 1 − Import sklearn.datasets.make_biclusters and matplotlib. Step 2 − Set the figure size Step 3 − Create data points namely data, row, and column. Step 4 − Create a plotter to show the array with constant block diagonal structure. Step 5 − Provide ... Read More