Data Science and Machine Learning Essentials – Module1- Regression
Simple Linear Regression
When operating on the data in the training and test data-sets, the difference between the known yvalue and the value calculated by f(x)must be minimized. In other words, for a single entity, y – f(x) should be close to zero. This is controlled by the values of any baseline variables (b) used in the function.
For various reasons, computers work better with smooth functions than with absolute values, so the values are squared. In other words, (y – f(x))2 should be close to zero.
To apply this rule to all rows in the training or test data, the squared values are summed over the whole dataset, so ∑(yi – f(xi))2 should be close to zero. This quantity is known as the sum of squares errors (or SSE). The regression algorithm minimizes this by adjusting the values of baseline variables (b) used in the function.
In statistics, the residual sum of squares (RSS), also known as the sum of squared residuals (SSR) or the sum of squared errors of prediction (SSE).
Minimizing the SSE for simple linear regression models (where there is only a single x value) generally works well, but when there are multiple x values, it can lead to over-fitting. To avoid this, the you can use ridge regression, in which the regression algorithm adds a regularization term to the SSE. This helps achieve a balance of accuracy and simplicity in the function.
Note those two terms up there:
Minimizing the first term, is just asking the computer to keep predicting the truth based on the training set.
Minimizing the second term, is like asking to keep the model “simple” – Principle of Occam’s Razor.
Support vector machine regression is an alternative to ridge regression that uses a similar technique to minimize the difference between the predicted f(x) values and the known y values.
To determine the best algorithm for a specific set of data, you can use cross-validation (CV), in which the data is divided into folds, with each fold in turn being used to test algorithms that are trained on the other folds.
To compare algorithms, the algorithms that performed the best was the one with the best average out-of-sample performance across the 10 test folds. Also one can check the standard deviation of each folds.
To determine the optimal value for a parameter (k) for an algorithm, you can use nested cross-validation, in which one of the folds in the training set is used to validate all possible k values. This process is repeated so that each fold in the training set is used as a validation fold. The best result is then tested with the test fold, and the whole process is repeated again until every fold has been used as the test fold.