Tag: scikit-learn

Total 29 Posts

Generating Synthetic Data with Numpy and Scikit-Learn

Introduction

In this tutorial, we'll discuss the details of generating different synthetic datasets using Numpy and Scikit-learn libraries. We'll see how different samples can be generated from various distributions with known parameters.

We'll also discuss generating datasets for different purposes, such as regression, classification, and clustering. At the end we'll

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Kernel Density Estimation in Python Using Scikit-Learn

Introduction

This article is an introduction to kernel density estimation using Python's machine learning library scikit-learn.

Kernel density estimation (KDE) is a non-parametric method for estimating the probability density function of a given random variable. It is also referred to by its traditional name, the Parzen-Rosenblatt Window method, after its

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Ensemble/Voting Classification in Python with Scikit-Learn

Introduction

Ensemble classification models can be powerful machine learning tools capable of achieving excellent performance and generalizing well to new, unseen datasets.

The value of an ensemble classifier is that, in joining together the predictions of multiple classifiers, it can correct for errors made by any individual classifier, leading to

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Gradient Boosting Classifiers in Python with Scikit-Learn

Introduction

Gradient boosting classifiers are a group of machine learning algorithms that combine many weak learning models together to create a strong predictive model. Decision trees are usually used when doing gradient boosting. Gradient boosting models are becoming popular because of their effectiveness at classifying complex datasets, and have recently

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