“Python Maschinelles Lernen” Code-Antworten

maschinelles Lernen Python

import pandas as pd
from sklearn.tree import DecisionTreeClassifier

hashdata = pd.read_csv("filename.csv")
X = hashdata.drop(columns=["output_column_name"])
y = hashdata.drop(columns=["input_column_name"])
model=DecisionTreeClassifier()
model.fit(X,y)
predictions=model.predict([[input]])
print(predictions)

Blue Bee

Python Maschinelles Lernen

# 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))

Ruben Visser

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