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Tune Hyperparameters for Classification Machine Learning Algorithms
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...
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Best Results for Standard Machine Learning Datasets
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...
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4 Distance Measures for Machine Learning
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...
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Train-Test Split for Evaluating Machine Learning Algorithms
The train-test split procedure is used to estimate the performance of machine learning algorithms when they are used to make predictions on data not used to train the model. It is a fast and easy...
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LOOCV for Evaluating Machine Learning Algorithms
The Leave-One-Out Cross-Validation, or LOOCV, procedure is used to estimate the performance of machine learning algorithms when they are used to make predictions on data not used to train the model. It...
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Nested Cross-Validation for Machine Learning with Python
The k-fold cross-validation procedure is used to estimate the performance of machine learning models when making predictions on data not used during training. This procedure can be used both when...
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How to Configure k-Fold Cross-Validation
The k-fold cross-validation procedure is a standard method for estimating the performance of a machine learning algorithm on a dataset. A common value for k is 10, although how do we know that this...
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Repeated k-Fold Cross-Validation for Model Evaluation in Python
The k-fold cross-validation procedure is a standard method for estimating the performance of a machine learning algorithm or configuration on a dataset. A single run of the k-fold cross-validation...
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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...
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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...
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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...
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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....
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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...
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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...
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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...
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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...
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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...
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Automated Machine Learning (AutoML) Libraries for Python
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...
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Multi-Core Machine Learning in Python With Scikit-Learn
Many computationally expensive tasks for machine learning can be made parallel by splitting the work across multiple CPU cores, referred to as multi-core processing. Common machine learning tasks that...
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Linear Discriminant Analysis With Python
Linear Discriminant Analysis is a linear classification machine learning algorithm. The algorithm involves developing a probabilistic model per class based on the specific distribution of observations...
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