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AUC of Random forest model is lower after tuning parameters using hypergrid search and CV with 10 folds


AUC of Random forest model is lower after tuning parameters using hypergrid search and CV with 10 folds

By : tmblweed
Date : October 23 2020, 08:10 AM
I wish this help you I expect 2 possible reasons for this.
Max-depth is set as None in the former model, which means nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples whereas max_depth=4 in the later, which makes the model less flexible.
code :


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Tuning two parameters for random forest in Caret package

Tuning two parameters for random forest in Caret package


By : LeoLee
Date : March 29 2020, 07:55 AM
I wish did fix the issue. You have to create a custom RF using the random forest package and then include the param that you want to include.
code :
customRF <- list(type = "Classification", library = "randomForest", loop = NULL)
customRF$parameters <- data.frame(parameter = c("mtry", "ntree"), class = rep("numeric", 2), label = c("mtry", "ntree"))
customRF$grid <- function(x, y, len = NULL, search = "grid") {}
customRF$fit <- function(x, y, wts, param, lev, last, weights, classProbs, ...) {
    randomForest(x, y, mtry = param$mtry, ntree=param$ntree, ...)
}
customRF$predict <- function(modelFit, newdata, preProc = NULL, submodels = NULL)
    predict(modelFit, newdata)
customRF$prob <- function(modelFit, newdata, preProc = NULL, submodels = NULL)
    predict(modelFit, newdata, type = "prob")
customRF$sort <- function(x) x[order(x[,1]),]
customRF$levels <- function(x) x$classes
customRF
Tuning Random Forest classifier

Tuning Random Forest classifier


By : Sujeet Kumar
Date : March 29 2020, 07:55 AM
Hope that helps There are two problems I see here. One is, like Rachel said, you've definitely been over-fitting your data. 80 is a really deep tree! That would give each node 2^80 possible leaves, or 1 followed by 24 zeros! Since you only have 100k+ samples, you're definitely giving a perfect fit on each tree to its respective bootstrap of the training data. Once you have enough depth to do this, further increases in depth limit doesn't do anything, and you're significantly past that point. This is undesirable.
Since even a (balanced) tree of depth 2^17 is 130k leaf nodes, you should look at some depths that are shallower than 17. Once you have a reasonable depth, max_features=1 will probably no longer be optimal. You should also test some different values for that.
Cross validation dataset folds for Random Forest feature importance

Cross validation dataset folds for Random Forest feature importance


By : HugoDen
Date : March 29 2020, 07:55 AM
I wish this help you I am trying to generate random forest's feature importance plot using cross validation folds. When only feature (X) and target(y) data is used, the implementation is straightforward such as: , This is the code that worked for me:
code :
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import KFold
from sklearn.datasets import make_classification

# classification dataset
data_x, data_y = make_classification(n_features=9)

# feature names must be declared outside the function
# feature_names = list(data_x.columns)

kf = KFold(n_splits=10)
rfc = RandomForestClassifier()
count = 1
# test data is not needed for fitting
for train, _ in kf.split(data_x, data_y):
    rfc.fit(data_x[train, :], data_y[train])
    # sort the feature index by importance score in descending order
    importances_index_desc = np.argsort(rfc.feature_importances_)[::-1]
    feature_labels = [feature_names[-i] for i in importances_index_desc]

    # plot
    plt.figure()
    plt.bar(feature_labels, rfc.feature_importances_[importances_index_desc])
    plt.xticks(feature_labels, rotation='vertical')
    plt.ylabel('Importance')
    plt.xlabel('Features')
    plt.title('Fold {}'.format(count))
    count = count + 1
plt.show()
Random Forest tuning with RandomizedSearchCV

Random Forest tuning with RandomizedSearchCV


By : suresh inchur
Date : March 29 2020, 07:55 AM
it helps some times I have a few questions concerning Randomized grid search in a Random Forest Regression Model. My parameter grid looks like this: , Add the 'scoring'-parameter to RandomizedSearchCV.
code :
RandomizedSearchCV(scoring="neg_mean_squared_error", ...
cv_results = rf_random.cv_results_
for mean_score, params in zip(cv_results["mean_test_score"], cvres["params"]):
    print(np.sqrt(-mean_score), params)
Hyperparameter Tuning in Random forest

Hyperparameter Tuning in Random forest


By : leo
Date : March 29 2020, 07:55 AM
Hope this helps
Why are the results of tuned model worst than the model with default parameters even when I am using RandomSearchCV and GridSearchCV. Ideally the model should give good results when tuned with cross-validation
code :
 {'max_depth': hp.choice('max_depth', range(1, 100)),
    'max_features': hp.choice('max_features', range(1, x_train.shape[1])),
    'min_samples_split': hp.uniform('min_samples_split', 0.1, 1)}
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