When you make an accurate prediction using your trained Machine Learning model, then the next step is often to measure the performance of your model. Data Scientists and other Machine Learning Experts spend a larger part to evaluate a Machine Learning model because they want to make sure that the model performs best in every sense before they use it practically.
There are a lot of ways to evaluate a Machine Learning model. In this article, I will take you through the 4 best ways to evaluate the performance of your Machine Learning model.
Best Ways to Evaluate A Machine Learning Model
The methods below are not better than each other according to the order I have given them. They all can be used according to the situation, that you will learn after practising all these methods. So here are the 4 best methods to evaluate a machine learning model:
One of the best ways to evaluate the performance of a classification model is to look at the confusion matrix algorithm. The idea behind a Confusion Matrix is to count the number of times instances of class X are classified as class Y.
To calculate the confusion matrix, you first need to have a set of predictions so that they can be compared with the actual targets. You can learn to implement the Confusion Matix by working on this example of Fraud Detection Model.
Precision and Recall
In Machine Learning, Precision and Recall are the two most important metrics for Model Evaluation. Precision represents the percentage of the results of your model, which are relevant to your model.
The recall represents the percentage total of total pertinent results classified correctly by your machine learning algorithm. You can learn to implement the Precision and Recall to evaluate a machine learning model from here.
Validation Set is used to evaluate the model’s hyperparameters. Our machine learning model will go through this data, but it will never learn anything from the validation set. A Data Scientist uses the results of a Validation set to update higher level hyperparameters.
We can use a validation set with the help of the cross-validation method in machine learning. You can learn to implement the cross-validation algorithm to evaluate the performance of a model from here.
The Roc Curve
The Receiver Operating Characteristic (ROC) curve is a popular tool used with binary classifiers. It is very similar to the precision/recall curve. Still, instead of plotting precision versus recall, the ROC curve plots the true positive rate (another name for recall) against the false positive rate (FPR).
The FPR is the ratio of negative instances that are incorrectly classified as positive. It is equal to 1 — the true negative rate (TNR), which is the ratio of negative cases that are correctly classified as negative. The TNR is also called specificity. You can learn to implement the Roc Curve algorithm to evaluate the performance of a machine learning model from here.
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