Metrics

This category groups posts on metrics used in machine learning. Each post focuses on a specific metric. The emphasis is on understanding how these tools actually work at a technical level. Here you will learn how to use machine learning metrics so that you can interpret your models performance.

gini impurity

Explaining the Gini Impurity with Examples in Python

Explaining the Gini Impurity with Examples in Python What is the Gini Impurity? The Gini Impurity is a loss function that describes the likelihood of misclassification for a single sample, according to the distribution of a certain set of labelled data. It is typically used within Decision Trees. More specifically, the Gini Impurity is used …

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cross validation

A Complete Introduction to Cross Validation in Machine Learning

A Complete Introduction to Cross Validation in Machine Learning What is Cross Validation? A natural question to ask, when building any predictive model, is how good are the predictions? Having a clear, quantitative measure for the expected model performance, is a key element to any machine learning project.   Cross validation is a family of …

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Measure Performance of a Classification Model

6 Methods to Measure Performance of a Classification Model

6 Methods to Measure Performance of a Classification Model How do we Measure Performance of a Classification Model? In this post, we will cover how to measure performance of a classification model. Classification is one of the most common tasks in machine learning. This is the case where the dependent variable only has discrete values. …

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mean squared error

Mean Squared Error

Mean Squared Error Introduction In this post we’ll cover the Mean Squared Error (MSE), arguably one of the most popular error metrics for regression analysis. The MSE is expressed as:    (1) where are the model output and are the true values. The summation is performed over individual data points available in our sample. The advantage …

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bias and variance

Bias and Variance

Bias and Variance Introduction Machine learning models are developed primarily to produce good predictions for some desired quantity. What we want is a model that can generalise well the patterns in our data in order to make reasonable predictions. Two important measures of how well a model generalises are bias and variance: Bias is the tendency …

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mean absolute error

Mean Absolute Error

Mean Absolute Error Introduction With any machine learning project, it is essential to measure the performance of the model. What we need is a metric to quantify the prediction error in a way that is easily understandable to an audience without a strong technical background. For regression problems, the Mean Absolute Error (MAE) is just such …

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