Topic-18 | Evaluation Metrics for different model
Lecture 18 | Evaluation Metrics for different model Our previous topic was Linear Regression. Linear Regression is used to predict a number from continuous nominal or numeric values. The number we predict can be any number, it can be a positive number or a negative number, it can be a high value or a low value. A graphical representation of linear regression was also shown. In graphical representation there is plot, a studio plane, data points and a line that must have most of the data points around it. In order to measure the performance of graph, we also discussed the Mean Squared error and in order to nullify the effect of resultant zero root mean square error. In colab we trained a modal over a data set. An issue raised during its execution that the resultant value of Mean Squared Error was very high. Our today’s topic will start from the finding the reasons of this high value of Mean Squared Error. ...