amitha353 / machine-learning-regression

Machine-Learning-Regression

Machine-Learning-Regression

MODELS

1. Linear Regression - Simple and Multiple
2. Regularization - Ridge (L2), Lasso (L1)
3. Nearest neighbors and kernel regression

ALGORITHMS

1. Gradient Descent
2. Coordinate Descent

GENERAL CONCEPTS

1. Loss function
2. Bias-Variance Trade off
3. Cross Validation
4. Sparsity
5. Overfitting
6. Model selection
7. Feature selection

---

Information	Modules
Simple Regression	Module 1
Multiple Regression	Module 2
Assesing Performance	Module 3
Ridge Regression	Module 4
Feature Selection & Lasso	Module 5
Nearest Neighbor & Kernel Regression	Module 6

---

Simple Regression

• 1 input and fit a line to data. (intercept and the slope coefficients).

Cost of the line

• Residual sum of squares (RSS) - Sum of the square of difference between the original value and the predicted value.
• Use RSS to asses different fits to the model.
• Choose the best fit on the training data that minimizes over the "intercept" and "slope".

Gradient Descent

• Iterative Algorithm that moves in the direction of the negative gradient.
• for convex functions it converges to the optimum.

---

Multiple Regression

• Allows to fit more complicated relationships between single input and output. Example - polynomial regression, seasonality, etc.
• It also incorporates more inputs and features and using these various inputs to compute the prediction.
• It is the sum of the weighted collection of features h of inputs xi + epsilon (error / noise term).

Cost -> RSS for multiple regression

• RSS for the coefficients -> sum of the square of the difference between the output and the predicted value.
• Predicted value = transpose of the feature matrix and coeffcients.

Gradient Descent

• The gradient is used for the closed-form solution as well. Complexity of inverse: O(D^3) -> D - #features.
• Gradient of the RSS.
• Requires a step-size.

---

Assesing Performance

• Variours measure to assess the efficieny of the model fit.

Measuring Loss

• It is a measure of how good the fit is performing.
• It is the cost of using estimated parameters w-hat at x when y is true.
• Absolute error - symmetric error - Absolute difference between true and predicted values.
• Squared error - symmetric error - Squared difference between the actual and predicted values.

3 Measures of errors

1. Training Error - Average over the loss measure pf the training dataset. Not a good predictive performance on the model.
2. Generalization / True Error - Measure of how well the error is being predicted for every possible observation available. It can't be computed.
3. Test Error - Examines the traing data fit on the test set. It is a noisy approximation to the generalization error.

Error xs. Model complexity

• Training error - decreases with model complexity.
• Generalization error - decreases and then increases with model complexity.
• Test error - noisy generalization of the true error.

Overfit

• If the training error is decrease below certain amount and the true error increases.
• At this point the magnitude of the coefficients increases.

3 source of prediction error

1. Noise - inherent to the model, cannot be controlled.
2. Bias - Measure of how well the model fits the true prediction / relationship by averaging over all possible training data sets.
3. Variance - Measure of how a fitted function vary from the training data set to training set of all size and observations.

Bias-Variance tradeoff

• Require low bias and low variance to have good predictive performance.
• Model complexity increases -> bias decreases and variance increases.
• Mean Square Error (MSE) = bias-variance tradeoff = bias^2 + variance.

Model selection and Assessment

• Fit the model on the training data set.
• Select between different models on the validation set.
• Test the performance on the test data.

---

Ridge Regression

• As model complexity increases, the models become overfit.
• Symptom of overfitting -> magnitude of coefficients increases.
• It trades of between the bias and the variance.
• Ridge total cost = measure of fit(RSS on training data) + measure of the magnitude of the coefficients.
• It is the L2 regularization parameter = Rss(w) + lambda * ||w||^2

Coefficient path

• The magnitude of the coefficients decreases with increases in the tuning parameter "lambda".

Ridge closed-form solution -> complexity O(D^3);

Cross-Validation

• In case of insuuffient data to form a separate validation set.
• Then perform k-fold cross validation.
• Here the training set is divided into blocks and each block is treated as the validation set.
  • training block -> parameters or coefficients are extimated.
  • validation block the error is computed.
• The average error across all validation set is computed.

---

Feature Selection & Lasso

Various methods to search over models with different number of features.

• All Subset - exhaustive approach, where feature combinations with least RSS is chosen.
• Greedy Algorithm - forward selection - suboptimal solution but eventually provides the desired model set and is more efficient.

Lasso objective function - L1 regularized regression

• It leads to sparse solutions.
• L1 norm = RSS(w) + lambda ||w||

Coefficient path

• Here the coefficient path becomes sparser with increasing lambda value. This provideds better feature solutions.

Coordinate Descent

• Better model since it is difficult to find the derivate of an absolute value. Need to use sub-gradients, alternative is coordinate descent.
• Iterate through the different dimensions of the objective or different features of the regression model.
• The coefficients for lasso was setup based on "soft-thresholding" - provides sparse solutions.

---

Nearest Neighbor & Kernel Regression - Nonparametric fits

1-NN - simple procedure

• Look for the most similar dataset observation and base the predictions on it.

Weighted k-NN

• weigh the more similar observations more than those less similar in the list of k-NN.
• Average across the rating to form the estimated prediction.

Kernel Regression

• Weight all the points rather than just weighting NN.
• The kernels have a bandwidth - lambda, outside which the observations are 0. Within the range/bandwidth also the observations can decay based on how far they are from the target point.
• It leads to local constant fits.
• Parametric fits -> global constant fits.

---