1 Linear Regression

1. R-squared is a goodness-of-fit measure for linear regression models. This statistic indicates the percentage of the variance in the dependent variable that the independent variables explain collectively. R-squared measures the strength of the relationship between your model and the dependent variable on a convenient 0 – 100% scale.

1.1. R2 value 0 means the prediction is mean value. R2 greater than 0 shows how much better it is compared to the mean with 1 being the perfet value - hence 1 captures every variablility in the dependent variable

1.1.1. R2 score can not just be used in linear regression but also other regressors

2. model evaluation: Error between the predicted values and the observed values are residuals RMSE is the square root of the average of the sum of the squares of residuals.

2.1. https://towardsdatascience.com/what-are-the-best-metrics-to-evaluate-your-regression-model-418ca481755b https://towardsdatascience.com/evaluation-metrics-model-selection-in-linear-regression-73c7573208be https://www.analyticsvidhya.com/blog/2021/05/know-the-best-evaluation-metrics-for-your-regression-model/ https://www.aionlinecourse.com/tutorial/machine-learning/evaluating-regression-models-performance https://medium.com/analytics-vidhya/evaluation-metrics-for-regression-models-c91c65d73af https://www.enjoyalgorithms.com/blog/evaluation-metrics-regression-models

3. definition and description: Linear regression assumes linear relationship between independent and dependent variables.

4. Links to some good articles: https://towardsdatascience.com/linear-regression-using-python-b136c91bf0a2 https://towardsdatascience.com/complete-guide-to-linear-regression-in-python-d95175447255 https://365datascience.com/tutorials/python-tutorials/linear-regression/ https://www.kdnuggets.com/2020/10/guide-linear-regression-models.html https://www.visual-design.net/post/a-simple-practical-guide-to-linear-regression https://builtin.com/machine-learning/cost-function https://becominghuman.ai/data-science-the-smart-way-regression-600bb49a391e