Resampling Methods - Part 3 - Estimation with Cross-Validation

Cross-validation is a statistical method used to estimate the skill of machine learning models. 

It is commonly used in applied machine learning to compare and select a model for a given predictive modeling problem because it is easy to understand, easy to implement, and results in skill estimates that generally have a lower bias than other methods. 

In this tutorial, you will discover a gentle introduction to the k-fold cross-validation procedure for estimating the skill of machine learning models. 

Resampling Methods - Part 2 - Estimation with Bootstrap

The bootstrap method is a resampling technique used to estimate statistics on a population by sampling a dataset with replacement. 

It can be used to estimate summary statistics such as the mean or standard deviation. 

It is used in applied machine learning to estimate the skill of machine learning models when making predictions on data not included in the training data.

In this tutorial, you will discover the bootstrap resampling method for estimating the skill of machine learning models on unseen data. 

Resampling Methods - Part 1 - Introduction to Resampling

Data is the currency of applied machine learning. Therefore, it is important that it is both collected and used effectively.

Data sampling refers to statistical methods for selecting observations from the domain with the objective of estimating a population parameter.

Whereas data resampling refers to methods for economically using a collected dataset to improve the estimate of the population parameter and help to quantify the uncertainty of the estimate.

Both data sampling and data resampling are methods that are required in a predictive modeling problem.

Data Visualization

Data visualization is an important skill in applied statistics and machine learning. This can be helpful when exploring and getting to know a dataset and can help with identifying patterns, corrupt data, outliers, and much more.

Examples Of Statistics In Machine Learning

Statistics and machine learning are two very closely related fields. In fact, the line between the two can be very fuzzy at times.

It would be fair to say that statistical methods are required to effectively work through a machine learning predictive modeling project.