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The 6 Metrics You Need to Optimize for Performance in Machine Learning - Exxact

#artificialintelligence

There are many metrics to measure the performance of your model depending on the type of machine learning you are looking to conduct. In this article, we take a look at performance measures for classification and regression models and discuss which is better optimized. Sometimes the metric to look at will vary according to the problem that is initially being solved. The True Positive Rate also called Recall is the go-to performance measure in binary/non-binary classification problems. Most if not all the time, we are only interested in correctly predicting one class.


The 6 Metrics You Need to Optimize for Performance in Machine Learning

#artificialintelligence

There are many metrics to measure the performance of your model depending on the type of machine learning you are looking to conduct. In this article, we take a look at performance measures for classification and regression models and discuss which is better optimized. Sometimes the metric to look at will vary according to the problem that is initially being solved. The True Positive Rate also called Recall is the go-to performance measure in binary/non-binary classification problems. Most if not all the time, we are only interested in correctly predicting one class.


Metrics to Evaluate your Machine Learning Algorithm

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Evaluating your machine learning algorithm is an essential part of any project. Most of the times we use classification accuracy to measure the performance of our model, however it is not enough to truly judge our model. In this post, we will cover different types of evaluation metrics available.


An in-depth guide to supervised machine learning classification

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In supervised learning, algorithms learn from labeled data. After understanding the data, the algorithm determines which label should be given to new data by associating patterns to the unlabeled new data. Supervised learning can be divided into two categories: classification and regression. Some examples of classification include spam detection, churn prediction, sentiment analysis, dog breed detection and so on. Some examples of regression include house price prediction, stock price prediction, height-weight prediction and so on.


14 Popular Evaluation Metrics in Machine Learning

#artificialintelligence

The evaluation metric is used to measure the performance of a machine learning model. A correct choice of an evaluation metric is very essential for a model. This article will cover all the metrics used in classification and regression machine learning models. For a classification machine learning algorithm, the output of the model can be a target class label or probability score. The different evaluation metric is used for these two approaches.