"decision tree" Code Answer's
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decision tree algorithm in python
Wide-eyed Whale
on May 23, 2020
# Create Decision Tree classifer object
clf = DecisionTreeClassifier(criterion="entropy", max_depth=3)
# Train Decision Tree Classifer
clf = clf.fit(X_train,y_train)
#Predict the response for test dataset
y_pred = clf.predict(X_test)
# Model Accuracy, how often is the classifier correct?
print("Accuracy:",metrics.accuracy_score(y_test, y_pred))
Source: www.datacamp.com
decision tree
Tame Tern
on May 06, 2020
from sklearn.datasets import load_iris
>>> from sklearn import tree
>>> X, y = load_iris(return_X_y=True)
>>> clf = tree.DecisionTreeClassifier()
>>> clf = clf.fit(X, y)Source: scikit-learn.org
decision trees
Average Albatross
on Jun 18, 2021
Training Data Set Accuracy: 0.9610705596107056
Training Data F1 Score 0.972027972027972
Validation Mean F1 Score: 0.6348494236272646
Validation Mean Accuracy: 0.7030561269468117
skitlearn decision tree
Homeless Herring
on Jan 05, 2021
from sklearn.datasets import load_iris
from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier(random_state=0)
iris = load_iris()
cross_val_score(clf, iris.data, iris.target, cv=10)Source: scikit-learn.org
Decision tree learning algorithm for classification
Ethercourt.ml
on Apr 03, 2020
# 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", "features"])
stringIndexer = StringIndexer(inputCol="label", outputCol="indexed")
si_model = stringIndexer.fit(df)
td = si_model.transform(df)
dt = DecisionTreeClassifier(maxDepth=2, labelCol="indexed")
model = dt.fit(td)
model.numNodes
# 3
model.depth
# 1
model.featuresImportances
# SparseVector(1, {0: 1.0})
model.numFeatures
# 1
model.numClasses
# 2
print(model.toDebugString)
# DecisionTreeClassificationModel (uid=...) of depth 1 with 3 nodes...
test0 = spark.createDataFrame([(Vectors.dense(-1.0),)], ["features"])
result = model.transform(test0).head()
result.prediction
# 0.0
result.probability
# DenseVectors([1.0, 0.0])
result.rawPrediction
# DenseVector([1.0, 0.0])
test1 = spark.createDataFrame([Vectors.sparse(1, [0], [1.0]),)], ["features"])
model.transform(test1).head().prediction
# 1.0
dtc_path = temp_path + "/dtc"
dt.save(dtc_path)
dt2 = DecisionTreeClassifier.load(dtc_path)
dt2.getMaxDepth()
# 2
model_path = temp_path + "/dtc_model"
model.save(model_path)
model2 = DecisionTreeClassificationModel.load(model_path)
model.featureImportances == model2.featureImportances
# TrueSource: spark.apache.org
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