Evaluation metrics for Regression

 Welcome to this post, you fall in very good place and interesting part of machine learning implementation. Evaluation- how we feel with this word, definitely we are going to examine something, i .e out machine learning model. feels very responsible state of mind- going to check and correcting our model.

when we are discussing about the machine learning, it divided into 3 parts supervised, unsupervised and reinforcement. we are not going to discuss in detail about this you can use other Resorces to learn or our other post.

supervised machine learning is of two types regression and classification. we will learn to evaluate these two separately in this post.

Evaluation metrics for Regression:

when we have made the regression model where our target value is continuous, we use following method to evaluate the model:

MAE: (mean absolute error)

MSE: (mean square error)

RMSE: (root mean square error)

R squared error

Adjusted R squared.

before we discussed about these techniques for evaluation it is mandatory for us to know why Evalution require.

sometimes we are not bother about the evaluation however it is necessary to check the performance of our model how well it is performing. regression model cannot have 100% accuracy otherwise it will be biased model. for this we will have to understand the concept of overfitting and underfitting. 

before deployment of machine learning model, it become necessary to evaluate the model to check performance to get the better result. when one model is good then we can consider other as well good, so no need to check other.

Here we will understand the advantage and disadvantages of all the evaluation techniques.