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B. Regression Models

Lectures
2
3
Difficulty
Medium
Homework
No
Small Description
Predicting continuous values.
journals
In the previous section you were introduced to the basics of machine learning, you were introduced to the simple linear regression and regularized linear regression models.
Linear methods are the most used model in finance, and in this section you will investigate more types of linear models.
After that we will look into non-linear regression models like KNN regression models (k-nearest neighbors algorithm).
We will not yet look at neural networks here, that would be left for another lecture.
Will not investigate tree and rule-based models here — these would be addressed in the classification section.
Many models can be used both in classification and regression tasks and sometimes just require one simple mathematical switch.
The purpose of this section is to give you a scope of how many different forms of regression models are available to us.
Linear Model Innovations
Purpose
Difficulty
Generalized Linear Model
Model to predict alternative values (count, categories, survival, skewed value) of different distributions as opposed to just continuous values with normal distributions.
Medium
Augmented Linear Model
Model with a preprocessing step to generate or transform features and labels to improve the fit of the model.
Easy
Penalized Augmentation Model
An automated method to augment the data as part of the modelling step while penalizing excessive feature creation.
Hard
Sampled Linear Models
A method to adjust the importance of different samples (instances) based on some criteria e.g., old data should be less important than recent data.
Medium
Gallery
Regression Models
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