When creating a graphical user interface (GUI) in Python with Tkinter, it's common to want to display data in a user-friendly format. One way to do this is to print a list of items to a Tkinter Text widget, which allows you to display the list with each item on ... Read More

Linear classification is one of the simplest machine learning problems. To implement linear classification, we will be using sklearn’s SGD (Stochastic Gradient Descent) classifier to predict the Iris flower species. Steps You can follow the below given steps to implement linear classification with Python Scikit-learn − Step 1 − First ... Read More

Iris, a multivariate flower dataset, is one of the most useful Pyhton scikit-learn datasets. It has 3 classes of 50 instances each and contains the measurements of the sepal and petal parts of three Iris species namely Iris setosa, Iris virginica, and Iris versicolor. Along with that Iris dataset contains ... Read More

Sklearn DIGITS dataset has 64 features as each image of the digit is of size 8 by 8 pixels. We can use Principal Component Analysis (PCA) to transform Scikit-learn DIGITS dataset into new feature space with 2 features. Transforming 64 features dataset to 2-feature dataset will be a big reduction ... Read More

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 ... 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 ... 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 ... 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 ... 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 ... 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 ... Read More