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Grid Searching seed in randomized machine learning

It's definitely an error to select an "optimal" random seed. If performance depends a lot on the random seed, it means that the the model always overfits, i.e. the patterns used by the model ...
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How to suppress "Estimator fit failed. The score on this train-test" warning message?

In the param_grid "max_features": [np.arange(0.3, 0.6, 0.1),'sqrt'], this code essentially means ...
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-1 votes

Automated feature selection - Best practice to avoid data leakage?

Take a look at this generic example, and see if it does what you want. ...
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1 vote

Prohibitive size of random forest when saved to disk

I ran into a similar issue and was surprised to find out that indeed decision trees can easily take a lot of memory (range of MBs) and random forests will easily multiply that in the GB range. Details ...
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Why can Random Forest "handle missing values and cardinality well compared to linear regression"?

Generally, random forests are a much more sophisticated method than linear regression: it's an ensemble method with multiple decision trees, and a single decision tree is already a much more flexible ...
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How to handle date in Random Forest prediction?

Random Forest needs to handle dates as numeric data, that's why you can use the day, the weekday, the month, the trimester and the year as separated fields. In addition to that, if your data has a ...
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1 vote

Which Model for predicting flight delays is appropriate except Random Forest and Decision Tree? (Monte Carlo?)

Weather is responsible for 90% of the flight delays. How is it possible to make reliable predictions with just 10% of the remaining causes? (if their data is available) You have an existing map called ...
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1 vote

Random Forest Classifier Output

On what data are you training on? Is your training data binary? If not, then set a treshold when your target variable should be 1 and 0 otherwise. Then train your RandomForestClassifier on the binary ...
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1 vote

Random Forest Classifier Output

Take the numbers given by the model and threshold them. Everything above X (usually .5) is mapped to 0, everything greater than X is mapped to 1.
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