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 properly assess, and interpret, your models performance.
Median Absolute Error What is the Median Absolute Error? The Median Absolute Error is a metric that can be used to quantify a regression models performance. This measure is slightly more difficult to interpret for a non-technical audience. However, the main benefit of using this quantity is its strong resilience to outliers. This is in …
3 Methods to Tune Hyperparameters in Decision Trees We can tune hyperparameters in Decision Trees by comparing models trained with different parameter configurations, on the same data. An optimal model can then be selected from the various different attempts, using any relevant metrics. There are several different techniques for accomplishing this task. Three of the …
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How to Measure Information Gain in Decision Trees For classification problems, information gain in Decision Trees is measured using the Shannon Entropy. The amount of entropy can be calculated for any given node in the tree, along with its two child nodes. The difference between the amount of entropy in the parent node, and the …
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[email protected] and [email protected] Made Easy with 1 Python Example What are [email protected] and [email protected] ? [email protected] and [email protected] are metrics used to evaluate a recommender model. These quantities attempt to measure how effective a recommender is at providing relevant suggestions to users. The typical workflow of a recommender involves a series of suggestions that will be …
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Explaining the Gini Impurity with Examples in Python This article will cover the Gini Impurity: what it is and how it is used. To make this discussion more concrete, we will then work through the implementation, and use, of the Gini Impurity in Python. What is the Gini Impurity? The Gini Impurity is a loss …
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A Complete Introduction to Cross Validation in Machine Learning This post will discuss various Cross Validation techniques. Cross Validation is a testing methodology used to quantify how well a predictive machine learning model performs. Simple illustrative examples will be used, along with coding examples in Python. What is Cross Validation? A natural question to ask, when …
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A Complete Guide to Implementing a PCA Biplot in Python In this post we will cover the complete implementation of a biplot in Python. We will build this functionality from the ground-up, and cover why you might want to use such a plot with PCA. What is a Biplot? In this post we will cover …
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6 Methods to Measure Performance of a Classification Model In this post, we will cover how to measure performance of a classification model. The methods discussed will involve both quantifiable metrics, and plotting techniques. How do we Measure Performance of a Classification Model? Classification is one of the most common tasks in machine learning. This …
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Coefficient of Determination Introduction The Coefficient of Determination is a metric for evaluating the goodness of fit for a linear regression model. This quantity is often defined as: (1) where is the sum of squared errors, and is the sum of squared total variance. Let’s define these, along with the sum of squared regression …
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 …