AutoML provides tools to automatically discover good machine learning model pipelines for a dataset with very little user intervention. It is ideal for domain experts new to machine learning or machine learning practitioners looking to get good results quickly for a predictive modeling task. Open-source libraries are available for using AutoML methods with popular machine […]

# Archive | Python Machine Learning

## Combined Algorithm Selection and Hyperparameter Optimization (CASH Optimization)

Machine learning model selection and configuration may be the biggest challenge in applied machine learning. Controlled experiments must be performed in order to discover what works best for a given classification or regression predictive modeling task. This can feel overwhelming given the large number of data preparation schemes, learning algorithms, and model hyperparameters that could […]

## Hyperparameter Optimization With Random Search and Grid Search

Machine learning models have hyperparameters that you must set in order to customize the model to your dataset. Often the general effects of hyperparameters on a model are known, but how to best set a hyperparameter and combinations of interacting hyperparameters for a given dataset is challenging. There are often general heuristics or rules of […]

## HyperOpt for Automated Machine Learning With Scikit-Learn

Automated Machine Learning (AutoML) refers to techniques for automatically discovering well-performing models for predictive modeling tasks with very little user involvement. HyperOpt is an open-source library for large scale AutoML and HyperOpt-Sklearn is a wrapper for HyperOpt that supports AutoML with HyperOpt for the popular Scikit-Learn machine learning library, including the suite of data preparation […]

## TPOT for Automated Machine Learning in Python

Automated Machine Learning (AutoML) refers to techniques for automatically discovering well-performing models for predictive modeling tasks with very little user involvement. TPOT is an open-source library for performing AutoML in Python. It makes use of the popular Scikit-Learn machine learning library for data transforms and machine learning algorithms and uses a Genetic Programming stochastic global […]

## Auto-Sklearn for Automated Machine Learning in Python

Automated Machine Learning (AutoML) refers to techniques for automatically discovering well-performing models for predictive modeling tasks with very little user involvement. Auto-Sklearn is an open-source library for performing AutoML in Python. It makes use of the popular Scikit-Learn machine learning library for data transforms and machine learning algorithms and uses a Bayesian Optimization search procedure […]

## Scikit-Optimize for Hyperparameter Tuning in Machine Learning

Hyperparameter optimization refers to performing a search in order to discover the set of specific model configuration arguments that result in the best performance of the model on a specific dataset. There are many ways to perform hyperparameter optimization, although modern methods, such as Bayesian Optimization, are fast and effective. The Scikit-Optimize library is an […]

## How to Calculate the Bias-Variance Trade-off with Python

The performance of a machine learning model can be characterized in terms of the bias and the variance of the model. A model with high bias makes strong assumptions about the form of the unknown underlying function that maps inputs to outputs in the dataset, such as linear regression. A model with high variance is […]

## Plot a Decision Surface for Machine Learning Algorithms in Python

Classification algorithms learn how to assign class labels to examples, although their decisions can appear opaque. A popular diagnostic for understanding the decisions made by a classification algorithm is the decision surface. This is a plot that shows how a fit machine learning algorithm predicts a coarse grid across the input feature space. A decision […]

## How to use Seaborn Data Visualization for Machine Learning

Data visualization provides insight into the distribution and relationships between variables in a dataset. This insight can be helpful in selecting data preparation techniques to apply prior to modeling and the types of algorithms that may be most suited to the data. Seaborn is a data visualization library for Python that runs on top of […]