Archive | Python Machine Learning

Boxplot of top 10 Spot-Checking Algorithms on a Classification Problem

How to Develop a Reusable Framework to Spot-Check Algorithms in Python

Spot-checking algorithms is a technique in applied machine learning designed to quickly and objectively provide a first set of results on a new predictive modeling problem. Unlike grid searching and other types of algorithm tuning that seek the optimal algorithm or optimal configuration for an algorithm, spot-checking is intended to evaluate a diverse set of […]

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Example ROC Curve

A Gentle Introduction to Probability Scoring Methods in Python

How to Score Probability Predictions in Python and Develop an Intuition for Different Metrics. Predicting probabilities instead of class labels for a classification problem can provide additional nuance and uncertainty for the predictions. The added nuance allows more sophisticated metrics to be used to interpret and evaluate the predicted probabilities. In general, methods for the […]

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Calibrated and Uncalibrated SVM Reliability Diagram

How and When to Use a Calibrated Classification Model with scikit-learn

Instead of predicting class values directly for a classification problem, it can be convenient to predict the probability of an observation belonging to each possible class. Predicting probabilities allows some flexibility including deciding how to interpret the probabilities, presenting predictions with uncertainty, and providing more nuanced ways to evaluate the skill of the model. Predicted […]

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Line Plot of ROC Curve

How and When to Use ROC Curves and Precision-Recall Curves for Classification in Python

It can be more flexible to predict probabilities of an observation belonging to each class in a classification problem rather than predicting classes directly. This flexibility comes from the way that probabilities may be interpreted using different thresholds that allow the operator of the model to trade-off concerns in the errors made by the model, […]

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Scatter plot of Moons Test Classification Problem

How to Generate Test Datasets in Python with scikit-learn

Test datasets are small contrived datasets that let you test a machine learning algorithm or test harness. The data from test datasets have well-defined properties, such as linearly or non-linearity, that allow you to explore specific algorithm behavior. The scikit-learn Python library provides a suite of functions for generating samples from configurable test problems for […]

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How to Install a Python 3 Environment on Mac OS X for Machine Learning and Deep Learning

How to Install a Python 3 Environment on Mac OS X for Machine Learning and Deep Learning

It can be difficult to install a Python machine learning environment on Mac OS X. Python itself must be installed first, and then there are many packages to install, and it can be confusing for beginners. In this tutorial, you will discover how to setup a Python 3 machine learning and deep learning development environment […]

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How to Setup a Python Environment for Machine Learning and Deep Learning with Anaconda

How to Setup a Python Environment for Machine Learning and Deep Learning with Anaconda

It can be difficult to install a Python machine learning environment on some platforms. Python itself must be installed first and then there are many packages to install, and it can be confusing for beginners. In this tutorial, you will discover how to set up a Python machine learning development environment using Anaconda. After completing […]

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Python3 Version

How to Create a Linux Virtual Machine For Machine Learning Development With Python 3

Linux is an excellent environment for machine learning development with Python. The tools can be installed quickly and easily and you can develop and run large models directly. In this tutorial, you will discover how to create and setup a Linux virtual machine for machine learning with Python. After completing this tutorial, you will know: […]

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