Course Wiki
Linear regression: vector form — Machine Learning Zoomcamp Module 2
The formula of linear regression can be synthesized with the dot product between features and weights. The feature vector includes the bias term with an x value of one, such as .
Related Pages
Machine Learning Zoomcamp › Module 2: Machine Learning for Regression › Linear regression: vector form
Notes
The formula of linear regression can be synthesized with the dot product between features and weights. The feature vector includes the bias term with an x value of one, such as $w_{0}^{x_{i0}},\ where\ x_{i0} = 1\ for\ w_0$.
When all the records are included, the linear regression can be calculated with the dot product between feature matrix and vector of weights, obtaining the y vector of predictions.
The entire code of this project is available in this jupyter notebook.
| ⚠️ |
The notes are written by the community. If you see an error here, please create a PR with a fix. |
Key concepts
Related notes
- mlz-m01-introduction-to-numpy
- mlz-m01-introduction-to-pandas
- mlz-m01-linear-algebra-refresher
- mlz-m01-setting-up-the-environment
- mlz-m02-baseline-model-for-car-price-prediction-project
- mlz-m02-car-price-prediction-project
- mlz-m02-car-price-prediction-project-summary
- mlz-m02-categorical-variables
- mlz-m02-computing-rmse-on-validation-data
- mlz-m02-data-preparation
- mlz-m02-exploratory-data-analysis
- mlz-m02-explore-more
- mlz-m02-feature-engineering
- mlz-m02-linear-regression
- mlz-m02-regularization
- mlz-m02-root-mean-squared-error-rmse
- mlz-m02-setting-up-the-validation-framework
- mlz-m02-training-linear-regression-normal-equation
- mlz-m02-tuning-the-model
- mlz-m02-using-the-model
- mlz-m03-data-preparation
- mlz-m03-eda
- mlz-m03-explore-more
- mlz-m03-feature-importance-churn-rate-and-risk-ratio
- mlz-m03-feature-importance-correlation
- mlz-m03-feature-importance-mutual-information
- mlz-m03-logistic-regression
- mlz-m03-model-interpretation
- mlz-m03-one-hot-encoding
- mlz-m03-setting-up-the-validation-framework
- mlz-m03-training-logistic-regression-with-scikit-learn
- mlz-m04-accuracy-and-dummy-model
- mlz-m04-confusion-table
- mlz-m04-cross-validation
- mlz-m04-roc-auc
- mlz-m04-roc-curves
- mlz-m04-summary
- mlz-m06-gradient-boosting-and-xgboost
- mlz-m06-xgboost-parameter-tuning
- mlz-m08-installation-of-tensorflow
- mlz-m10-creating-a-pre-processing-service