Book Image

Data Cleaning and Exploration with Machine Learning

By : Michael Walker
Book Image

Data Cleaning and Exploration with Machine Learning

By: Michael Walker

Overview of this book

Many individuals who know how to run machine learning algorithms do not have a good sense of the statistical assumptions they make and how to match the properties of the data to the algorithm for the best results. As you start with this book, models are carefully chosen to help you grasp the underlying data, including in-feature importance and correlation, and the distribution of features and targets. The first two parts of the book introduce you to techniques for preparing data for ML algorithms, without being bashful about using some ML techniques for data cleaning, including anomaly detection and feature selection. The book then helps you apply that knowledge to a wide variety of ML tasks. You’ll gain an understanding of popular supervised and unsupervised algorithms, how to prepare data for them, and how to evaluate them. Next, you’ll build models and understand the relationships in your data, as well as perform cleaning and exploration tasks with that data. You’ll make quick progress in studying the distribution of variables, identifying anomalies, and examining bivariate relationships, as you focus more on the accuracy of predictions in this book. By the end of this book, you’ll be able to deal with complex data problems using unsupervised ML algorithms like principal component analysis and k-means clustering.
Table of Contents (23 chapters)
1
Section 1 – Data Cleaning and Machine Learning Algorithms
5
Section 2 – Preprocessing, Feature Selection, and Sampling
9
Section 3 – Modeling Continuous Targets with Supervised Learning
13
Section 4 – Modeling Dichotomous and Multiclass Targets with Supervised Learning
19
Section 5 – Clustering and Dimensionality Reduction with Unsupervised Learning

Imputing values with regression

We ended the previous section by assigning a group mean to the missing values rather than the overall sample mean. As we discussed, this is useful when the feature that determines the groups is correlated with the feature that has the missing values. Using regression to impute values is conceptually similar to this, but we typically use it when the imputation will be based on two or more features.

Regression imputation replaces a feature's missing values with values predicted by a regression model of correlated features. This particular kind of imputation is known as deterministic regression imputation since the imputed values all lie on the regression line, and no error or randomness is introduced.  

One potential drawback of this approach is that it can substantially reduce the variance of the feature with missing values. We can use stochastic regression imputation to address this drawback. We will explore both approaches in this...