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May 19, 2015 · I heard that some random forest models will ignore features with nan values and use a randomly selected substitute feature. This doesn't seem to be the default behaviour in scikit learn though. Does anyone have a suggestion of how to achieve this behaviour? It is attractive because you do not need to supply an imputed value. –
Oct 20, 2016 · from sklearn.datasets import load_iris from sklearn.ensemble import RandomForestClassifier from sklearn import tree import pydotplus import os # Load the Iris dataset for demonstration iris = load_iris() X = iris.data y = iris.target # Create a random forest classifier clf = RandomForestClassifier(n_estimators=100) # Train the classifier clf.fit(X, y) # Create directory to save decision tree images os.makedirs("decision_trees", exist_ok=True) # Plot decision trees of the random forest for i ...
Jun 30, 2015 · I would like to get a confidence score of each of the predictions that it makes, showing on how sure the classifier is on its prediction that it is correct. I want something like this: How sure is the classifier on its prediction? Class 1: 81% that this is class 1 Class 2: 10% Class 3: 6% Class 4: 3% Samples of my code:
Mar 20, 2014 · So use sklearn.model_selection.GridSearchCV to test a range of parameters (parameter grid) and find the optimal parameters. You can use 'gini' or 'entropy' for the Criterion, however, I recommend sticking with 'gini', the default.
Nov 22, 2017 · I've been using sklearn's random forest, and I've tried to compare several models. Then I noticed that random-forest is giving different results even with the same seed. I tried it both ways: random.seed(1234) as well as use random forest built-in random_state = 1234 In both cases, I get non-repeatable results.
Jul 12, 2014 · Most implementations of random forest (and many other machine learning algorithms) that accept categorical inputs are either just automating the encoding of categorical features for you or using a method that becomes computationally intractable for large numbers of categories.
May 7, 2015 · You have to fit your data before you can get the best parameter combination. from sklearn.grid_search import GridSearchCV from sklearn.datasets import make_classification from sklearn.ensemble import RandomForestClassifier # Build a classification task using 3 informative features X, y = make_classification(n_samples=1000, n_features=10, n_informative=3, n_redundant=0, n_repeated=0, n_classes=2, random_state=0, shuffle=False) rfc = RandomForestClassifier(n_jobs=-1,max_features= 'sqrt' ,n ...
Apr 12, 2018 · After seeing the precision_recall_curve, if I want to set threshold = 0.4, how to implement 0.4 into my random forest model (binary classification), for any probability <0.4, label it as 0, for any >=0.4, label it as 1.
May 19, 2017 · I am using the below code to save a random forest model. I am using cPickle to save the trained model. As I see new data, can I train the model incrementally. Currently, the train set has about 2 ...
Dec 22, 2017 · from sklearn.ensemble import RandomForestClassifier clf = RandomForestClassifier(n_estimators=10) clf = clf.fit(df_train, df_train_labels) However, the last line fails with this error: raise ValueError("Unknown label type: %r" % y_type) ValueError: Unknown label type: 'continuous'
