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1 vote

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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0 votes

Machine learning and time-based data

I assume it depends on what information you want to get/what you're trying to capture/predict. E.g. are you looking to understand influence of season on shopping rates? Yearly data wouldn't be of use ...
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2 votes

Using Latitude/Longitude and site ID in classification of daily air pollution levels

Are classifiers built on the first dataset valid as they are basing some of their predictive value simply on the site ID? Is site ID (and to a similar extend Lat/Lon) a valid feature to include? It's ...
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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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3 votes

How to use LAT/LNG as predictor variables

If you want to predict at locations where you don't have data, and you assume that there is a continuous surface of your variable of interest (ie it is defined at all locations) then you can use ...
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0 votes

Marketing Spend Optimization Techniques

I would try portfolio optimization, where you maximize something like the expected return while minimizing costs like risk. To do that, you'll need to assign an expected return (for example, the ROI) ...
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  • 489
0 votes

How to predict probabilities in xgboost using R?

Many years late, but noticed this; and this is how I do it: pred_s <- predict(bst, x_mat_s2, type="prob") Typically then I then work with probability ...
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  • 101
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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