Root mean squared error or RMSE is a measure of the difference between actual values and predicted values of a machine learning modelĀ like Linear Regression. Root mean squared error is a measure of how well the machine learning model can perform. The lower the RMSE, the better the model. RMSE is always positive, and … Continue reading What is Root Mean Squared Error or RMSE

# Linear Regression

# What is R Squared for Linear Regression

For Linear Regression, R-squared is a statistical term which indicates how close the data are to the fitted regression line. R-Squared is also known as coefficient of determination. R-squared = Explained variation in data / Total variation in data R-squared = 1 - (RSS/TSS) RSS = Sum of squares of difference between predicted value and … Continue reading What is R Squared for Linear Regression

# Top 5 Assumptions of Linear Regression Algorithm

We can not implement Linear Regression on any given data. If we have got a dataset and we are planning to implement Linear Regression Model on that data we must first check if the given data is inline with the assumptions of Linear Regression. Once the given data agrees to all the assumptions then only … Continue reading Top 5 Assumptions of Linear Regression Algorithm

# Linear Regression using sklearn in 10 lines

Linear regression is one of the most popular and fundamental machine learning algorithm. If relationship between two variables are linear we can use Linear regression to predict one variable given that other is known. For example if we are researching how the price of the house will vary if we change the area of the … Continue reading Linear Regression using sklearn in 10 lines