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How and When to Use ROC Curves and Precision-Recall Curves for Classification...
It can be more flexible to predict probabilities of an observation belonging to each class in a classification problem rather than predicting classes directly. This flexibility comes from the way that...
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How and When to Use a Calibrated Classification Model with scikit-learn
Instead of predicting class values directly for a classification problem, it can be convenient to predict the probability of an observation belonging to each possible class. Predicting probabilities...
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A Gentle Introduction to Probability Scoring Methods in Python
How to Score Probability Predictions in Python and Develop an Intuition for Different Metrics. Predicting probabilities instead of class labels for a classification problem can provide additional...
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How to Develop a Reusable Framework to Spot-Check Algorithms in Python
Spot-checking algorithms is a technique in applied machine learning designed to quickly and objectively provide a first set of results on a new predictive modeling problem. Unlike grid searching and...
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How to Fix FutureWarning Messages in scikit-learn
Upcoming changes to the scikit-learn library for machine learning are reported through the use of FutureWarning messages when the code is run. Warning messages can be confusing to beginners as it looks...
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Python Machine Learning Mini-Course
From Developer to Machine Learning Practitioner in 14 Days Python is one of the fastest-growing platforms for applied machine learning. In this mini-course, you will discover how you can get started,...
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Python is the Growing Platform for Applied Machine Learning
You should pick the right tool for the job. The specific predictive modeling problem that you are working on should dictate the specific programming language, libraries and even machine learning...
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How to Create a Linux Virtual Machine For Machine Learning Development With...
Linux is an excellent environment for machine learning development with Python. The tools can be installed quickly and easily and you can develop and run large models directly. In this tutorial, you...
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Your First Machine Learning Project in Python Step-By-Step
Do you want to do machine learning using Python, but you’re having trouble getting started? In this post, you will complete your first machine learning project using Python. In this step-by-step...
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How to Save a NumPy Array to File for Machine Learning
Developing machine learning models in Python often requires the use of NumPy arrays. NumPy arrays are efficient data structures for working with data in Python, and machine learning models like those...
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How to Connect Model Input Data With Predictions for Machine Learning
Fitting a model to a training dataset is so easy today with libraries like scikit-learn. A model can be fit and evaluated on a dataset in just a few lines of code. It is so easy that it has become a...
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How to Save and Reuse Data Preparation Objects in Scikit-Learn
It is critical that any data preparation performed on a training dataset is also performed on a new dataset in the future. This may include a test dataset when evaluating a model or new data from the...
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How to Perform Feature Selection with Categorical Data
Feature selection is the process of identifying and selecting a subset of input features that are most relevant to the target variable. Feature selection is often straightforward when working with...
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How to Choose a Feature Selection Method For 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...
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How to Use Out-of-Fold Predictions in Machine Learning
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...
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How to Develop Super Learner Ensembles in Python
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...
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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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How to Transform Target Variables for Regression With Scikit-Learn
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...
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Results for Standard Classification and Regression 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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Use the ColumnTransformer for Numerical and Categorical Data in Python
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...
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