By doing this, the probability calculated is biased, because when the data is resampled, the proportion of positive/ negative observations (predicted probability) changes in each group due to resampling. If the predicted probability of Random Forest is considered 'valid': when facing the imbalanced data, one way to improve the performance of RF is to use downsampling technique on the training data set before making trees (resampling the data in such a way that the positive and negative class are 'balanced' in proportion).

averaging the predicted probability of many trees.īut can this correct all the bias made by every single tree? If the correction of the predicted probability by this technique works? Can the same idea be applied to stepwise regression to correct its weakness cited here by and question is applied for other data mining methods as well.adding randomness to the model (variable selected at each nodes, bootstrapped training data sets).

IMHO: the predicted probability calculated by each single tree of RF is highly biased (because of data dredging), then RF tries to correct this bias by But does the predicted probability calculated by Random Forest make sense when it involve the using of data mining to find a patterns in data that can be significant in some extent? Although there is no hypothesis testing nor use of p-value. I'm thinking of Random Forest when reading this definition. The process of data mining involves automatically testing huge numbers of hypotheses about a single data set by exhaustively searching for combinations of variables that might show a correlation. Hypothesis as to the underlying causality. Use of data mining to uncover patterns in data that can be presentedĪs statistically significant, without first devising a specific In the wikipedia definition of data dredging:ĭata dredging (also data fishing, data snooping, and p-hacking) is the

Data dredging/p-hacking