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Python 2022-02-01 20:30:14
random forrest plotting feature importance function
def plot_feature_importances(model): n_features = data_train.shape[1] plt.figure(figsize=(20,20)) plt.barh(range(n_features), model.feature_importances_, align='center') plt.yticks(np.arange(n_features), data_train.columns.values) pl... Add solution -
Python 2021-10-21 15:44:12
feature_importances_ sklearn
print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn.datasets import make_classification from sklearn.ensemble import ExtraTreesClassifier # Build a classification task using 3 informative features X, y = make_classification(n... Add solution -
Python 2021-10-21 09:39:12
pca feature selection
# Feature Importance with Extra Trees Classifier from pandas import read_csv from sklearn.ensemble import ExtraTreesClassifier # load data url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.csv" names = ['pr... Add solution -
Other 2021-10-20 08:32:13
sklearn random forest feature importance
import pandas as pd forest_importances = pd.Series(importances, index=feature_names) fig, ax = plt.subplots() forest_importances.plot.bar(yerr=std, ax=ax) ax.set_title("Feature importances using MDI") ax.set_ylabel("Mean decrease in impuri... Add solution -
Python 2021-10-12 17:12:04
Gradient-Boosted Trees (GBTs) learning algorithm for classification
# Gradient-Boosted Trees (GBTs) learning algorithm for classification from numpy import allclose from pyspark.ml.linalg import Vectors from pyspark.ml.feature import StringIndexer df = spark.createDataFrame([ (1.0, Vectors.dense(1.0)), (0.0, Vectors.... Add solution -
Python 2021-09-17 18:24:02
Decision tree learning algorithm for classification
# Decision tree learning algorithm for classification from pyspark.ml.linalg import Vectors from pyspark.ml.feature import StringIndexer df = spark.createDataFrame([ (1.0, Vectors.dense(1.0)), (0.0, Vectors.sparse(1, [], []))], ["label", &q... Add solution