案例学习：预测房价

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案例学习：预测房价

Linear Regression, Ridge Regression, Lasso (Statistics), Regression Analysis

4.8（4,241 個評分）

- 5 stars3,442 ratings
- 4 stars679 ratings
- 3 stars70 ratings
- 2 stars18 ratings
- 1 star32 ratings

Apr 07, 2016

This is an excellent course. The presentation is clear, the graphs are very informative, the homework is well-structured and it does not beat around the bush with unnecessary theoretical tangents.

Jan 02, 2017

This course is great. Things are very clearly explained. I am particularly happy because it helped me to understand many mathematical concepts. I will try not to be scared about formulas anymore.

從本節課中

Feature Selection & Lasso

A fundamental machine learning task is to select amongst a set of features to include in a model. In this module, you will explore this idea in the context of multiple regression, and describe how such feature selection is important for both interpretability and efficiency of forming predictions. <p> To start, you will examine methods that search over an enumeration of models including different subsets of features. You will analyze both exhaustive search and greedy algorithms. Then, instead of an explicit enumeration, we turn to Lasso regression, which implicitly performs feature selection in a manner akin to ridge regression: A complex model is fit based on a measure of fit to the training data plus a measure of overfitting different than that used in ridge. This lasso method has had impact in numerous applied domains, and the ideas behind the method have fundamentally changed machine learning and statistics. You will also implement a coordinate descent algorithm for fitting a Lasso model. <p>Coordinate descent is another, general, optimization technique, which is useful in many areas of machine learning.

#### Emily Fox

Amazon Professor of Machine Learning#### Carlos Guestrin

Amazon Professor of Machine Learning