Many machine learning algorithms perform better when numerical input variables are scaled to a standard range. This includes algorithms that […]

# Search results for "MinMaxScaler"

## Modeling Pipeline Optimization With scikit-learn

This tutorial presents two essential concepts in data science and automated learning. One is the machine learning pipeline, and the […]

## Prediction Intervals for Deep Learning Neural Networks

Prediction intervals provide a measure of uncertainty for predictions on regression problems. For example, a 95% prediction interval indicates that […]

## How to Develop a Neural Net for Predicting Car Insurance Payout

Developing a neural network predictive model for a new dataset can be challenging. One approach is to first inspect the […]

## Autoencoder Feature Extraction for Regression

Autoencoder is a type of neural network that can be used to learn a compressed representation of raw data. An […]

## Autoencoder Feature Extraction for Classification

Autoencoder is a type of neural network that can be used to learn a compressed representation of raw data. An […]

## Develop a Bagging Ensemble with Different Data Transformations

Bootstrap aggregation, or bagging, is an ensemble where each model is trained on a different sample of the training dataset. […]

## Radius Neighbors Classifier Algorithm With Python

Radius Neighbors Classifier is a classification machine learning algorithm. It is an extension to the k-nearest neighbors algorithm that makes […]

## How to Hill Climb the Test Set for Machine Learning

Hill climbing the test set is an approach to achieving good or perfect predictions on a machine learning competition without […]

## How to Selectively Scale Numerical Input Variables for Machine Learning

Many machine learning models perform better when input variables are carefully transformed or scaled prior to modeling. It is convenient, […]