In this article, we will discuss various metrics of regression and classification in machine learning. We always think of steps involved in modeling a good machine learning algorithm. The one-step is the metrics for evaluation of the goodness of the model. When we fit our model and make a prediction, then we always try to know the error and the accuracy. This article will try to deliver and explain various error measurement methods in regression and classification.

Fully Explained SVM Classification with Python

There are criteria to evaluate the prediction quality of the model as shown below:

• Metric functions: that we will study in this article.Estimator score method: this method has a score method to evaluate to solving the problem.Scoring parameter: The scoring parameters tells the estimator to choose the metric for evaluation of the model with grid_search.GridSearchCV and cross_validation.cross_val_score

Basic definition

• Estimator: It is a function or equation to predict the more accurate modeling points on real data points.

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