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On historical data, you can try to determine what would have happened if you add the model before. For each client you proposed some product, calculate the average gain (propensity*cost) of said product and compare with the average gain of said of best product. Say each product cost 100$. Last year you offered product1 to client1 but he refused. Using your ...


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In the case of Random Forest, a new tree is built without any input from the previously built trees. If the number of trees built is high, dropping any one tree when making a decision won't affect the final output of the random forest model unless the dropped tree holds information about an extreme outlier that impacts the ensemble model. In the case of ...


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Short answer: they are equivalent. Any results that suggest otherwise are due to random chance or due to modification of parameters other than the number of trees. A random forest is just a voted ensemble of decision trees. By default, each tree's vote is weighted equally and then these votes are averaged. Suppose sets X and Y are the same size. Then if you ...


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With a toy dataset, I obtained slightly better results with the RF 1000 trees import pandas as pd import numpy as np from sklearn.datasets import load_breast_cancer from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split from sklearn.metrics import roc_auc_score from scipy.stats import ks_2samp import ...


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In Random Forest, each trea of the forest is trained independant from the others. There's no relation between trees. To summarise very quickly, if you have a dataset with 10 attributes, each tree will select n (a parameter you have to fix) attributes among the 10, and create a basic decision tree (like C4.5 style) only with those n attributes knowledge. Then,...


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The two slightly-smaller models will perform exactly the same, on average. There is no difference baked in to the different trees: "the last tree will be the best trained" is not true. The only difference among the trees is the random subsample they work with and random effects while building the tree (feature subsetting, e.g.). Gradient boosted ...


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