From the course: Machine Learning with Scikit-Learn
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Which machine learning model should you use? - scikit-learn Tutorial
From the course: Machine Learning with Scikit-Learn
Which machine learning model should you use?
- [Instructor] With so many machine learning algorithms available from scikit-learn, which algorithm should you choose? Selecting a good enough model from among a large number of possible machine learning models is one of the hardest parts of machine learning. Some algorithms are better suited to different types of data and problems. Luckily, a quick answer to model selection with scikit-learn is, use the algorithm cheat sheet. It's meant to give you a rough guide in how to choose an algorithm. From the start point, you first ask, do you have more than 50 samples? From there, you keep on answering questions until you get an idea of what you should try. If you don't use the cheat sheet, here are a few things to consider when choosing a model. The first thing is a problem you're trying to solve. For example, if you have a supervised learning problem, figuring out if you're trying to predict a continuous or categorical value…
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Contents
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What is supervised learning?54s
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How to format data for scikit-learn1m 55s
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Linear regression using scikit-learn4m 32s
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Train test split1m 53s
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Logistic regression using scikit-learn3m 55s
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Logistic regression for multiclass classification3m 36s
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Decision trees using scikit-learn3m 9s
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How to visualize decision trees using Matplotlib2m 5s
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Bagged trees using scikit-learn2m
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Random forests using scikit-learn2m 41s
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Which machine learning model should you use?1m 23s
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