Tag Archives: Machine Learning

Linear Regression 5 – Other Practical Issues

In the previous articles we have discussed the basic concept of simple linear regression; how to measure the error of the regression model so that we can use the gradient descent method to find the global optimum of the regression problem; develop the multivariate linear regression model for real world problems; and how to choose… Read More »

Linear Regression 4 – Learning Rate and Initial Weights

Choosing Learning Rate We introduced an important parameter, the learning rate α, in Linear Regression 2 – Gradient Descent without discussing how to choose its value.  In fact, the choice of the learning rate affects the performance of the algorithm significantly.  It determines the convergence speed of the gradient descent algorithm, which is the number… Read More »

Linear Regression 3 – Multivariate Linear Regression

The Simple Linear Regression can only handle the relationship between the target feature and one descriptive feature, which is not often the case in real life.  For example, the number of features in the dataset of our toy example is now expanded to 4, including target feature Rental Price: Size Rental Price Floor Number of… Read More »

Linear Regression 2 – Gradient Descent

In the previous article, Linear Regression 1 – Simple Linear Regression and Cost Function, we introduced the concept of simple linear regression, which is basically to find a regression line model     so that the prediction Mw(x) is as close to the y of our training data (x,y) as possible.  To find the best… Read More »

Linear Regression 1 – Simple Linear Regression and Cost Function

Error-based Learning Imagine you are just starting to learn skipping and may occasionally trip over the rope or even fall down.  You then try to slightly adjust the jumping speed, height, strength of your arms, your foot balance skill, etc.  You may trip again and then continue adjusting your posture and movement.  And one day,… Read More »