Multioutput regression are regression problems that involve predicting two or more numerical values given an input example. An example might be to predict a coordinate given an input, e.g. predicting x and y values. Another example would be multi-step time series forecasting that involves predicting multiple future time series of a given variable. Many machine […]
Distance measures play an important role in machine learning. They provide the foundation for many popular and effective machine learning algorithms like k-nearest neighbors for supervised learning and k-means clustering for unsupervised learning. Different distance measures must be chosen and used depending on the types of the data. As such, it is important to know […]
Data cleaning is a critically important step in any machine learning project. In tabular data, there are many different statistical analysis and data visualization techniques you can use to explore your data in order to identify data cleaning operations you may want to perform. Before jumping to the sophisticated methods, there are some very basic […]
You must prepare your raw data using data transforms prior to fitting a machine learning model. This is required to ensure that you best expose the structure of your predictive modeling problem to the learning algorithms. Applying data transforms like scaling or encoding categorical variables is straightforward when all input variables are the same type. […]
It is important that beginner machine learning practitioners practice on small real-world datasets. So-called standard machine learning datasets contain actual observations, fit into memory, and are well studied and well understood. As such, they can be used by beginner practitioners to quickly test, explore, and practice data preparation and modeling techniques. A practitioner can confirm […]
Data preparation is a big part of applied machine learning. Correctly preparing your training data can mean the difference between mediocre and extraordinary results, even with very simple linear algorithms. Performing data preparation operations, such as scaling, is relatively straightforward for input variables and has been made routine in Python via the Pipeline scikit-learn class. […]
Machine learning algorithms have hyperparameters that allow you to tailor the behavior of the algorithm to your specific dataset. Hyperparameters are different from parameters, which are the internal coefficients or weights for a model found by the learning algorithm. Unlike parameters, hyperparameters are specified by the practitioner when configuring the model. Typically, it is challenging […]
Selecting a machine learning algorithm for a predictive modeling problem involves evaluating many different models and model configurations using k-fold cross-validation. The super learner is an ensemble machine learning algorithm that combines all of the models and model configurations that you might investigate for a predictive modeling problem and uses them to make a prediction […]
Machine learning algorithms are typically evaluated using resampling techniques such as k-fold cross-validation. During the k-fold cross-validation process, predictions are made on test sets comprised of data not used to train the model. These predictions are referred to as out-of-fold predictions, a type of out-of-sample predictions. Out-of-fold predictions play an important role in machine learning […]
Feature selection is the process of reducing the number of input variables when developing a predictive model. It is desirable to reduce the number of input variables to both reduce the computational cost of modeling and, in some cases, to improve the performance of the model. Feature-based feature selection methods involve evaluating the relationship between […]
