Data Analysis and Machine Learning: Linear Regression and more Advanced Regression Analysis
Contents
Regression analysis, overarching aims
General linear models
Rewriting the fitting procedure as a linear algebra problem
Rewriting the fitting procedure as a linear algebra problem, follows
Generalizing the fitting procedure as a linear algebra problem
Generalizing the fitting procedure as a linear algebra problem
Optimizing our parameters
Optimizing our parameters, more details
Interpretations and optimizing our parameters
Interpretations and optimizing our parameters
Interpretations and optimizing our parameters
The \( \chi^2 \) function
The \( \chi^2 \) function
The \( \chi^2 \) function
The \( \chi^2 \) function
The \( \chi^2 \) function
The \( \chi^2 \) function
Simple regression model
Simple regression model, now using
scikit-learn
Simple linear regression model using
scikit-learn
Simple linear regression model
Less noise
How to study our fits
Minimizing the cost function
Relative error
The richness of
scikit-learn
Functions in
scikit-learn
Other functions in
scikit-learn
The mean absolute error and other functions in
scikit-learn
Cubic polynomial in
scikit-learn
Simple regression model with gradient descent
Simple regression model with stochastic gradient descent
Polynomial Regression
Ridge and Lasso Regression
The singular value decompostion
Lasso and Ridge regression
Logistic regression
Lasso and Ridge regression
Discuss the mathematics here
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