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Why do we need XGBoost and Random Forest?

First Question: XGBoost converts weak learners to strong learners. What's the advantage of doing this? Combining many weak learners instead of just using a single tree? Just to get the vocabulary ...
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How far or close would feature importance information from an ML model is from causal diagrams?

Very var in general - they capture different things The crucial part of causal diagrams is identifying a graph encoding predictive relationships between variables that agree with their conditional ...
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How does XGBoost compute the probabilities in predict_proba()?

XGBoost is a gradient-boosting algorithm, which means it builds an ensemble of weak decision trees in a sequential manner, where each tree learns to correct the mistakes of the previous trees. To ...
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How to interpret importance of random forest model, Mean Decrease Accuracy and Mean Decrease Gini?

Good afternoon @FOH, making a quick summary of the selection of resources would be to remove the resources that are not useful for anything. It does not improve model accuracy. It has no relationship ...
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Random Forest on high correlated data

Welcome to the data science sector. Your three points seem to relate to different aspects. I will try to address all three: 1. Feature Importance To explain the effect, I would go the other way and ...
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ValueError: Input contains NaN, infinity or a value too large for dtype('float32')

Once Check X and y types. Both should be of the same kind.
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How a Random forest "learns" or How loss (objective function value) is propagated back so that a random forest can "Improve"?

In a random forest classifier, there is no backpropagated loss. Instead, the N trees are grown independently from each other and then, for a new prediction, a ...
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