Web• Have 6+ years of experience in ML and Deep Learning research. • Proficient in Machine Learning supervised & unsupervised algorithms like Ensemble, K-Means, DBSCAN, Linear and Logistic Regression, Decision Tree, SVM, Bayesian networks, etc. • Skilled in Neural Networks like CNN, RNN, GAN & Object Detection algorithms … decision_tree = tree.DecisionTreeClassifier () decision_tree = decision_tree.fit (var_train, res_train) Test model performance by calculating accuracy on test set: res_pred = decision_tree.predict (var_test) score = accuracy_score (res_test, res_pred) Or you could directly use decision_tree.score: score = decision_tree.score (var_test, res_test)

Decision Tree classification with 100% Accuracy Kaggle

WebJun 14, 2024 · This grid search builds trees of depth range 1 → 7 and compares the training accuracy of each tree to find the depth that produces the highest training accuracy. The most accurate tree has a depth of 4, shown in the plot below. This tree has 10 rules. This means it is a simpler model than the full tree. WebOct 8, 2024 · Decision Tree Implementation in Python As for any data analytics problem, we start by cleaning the dataset and eliminating all the null and missing values from the … climbing shelves

python - Getting 100% Accuracy on my DecisionTree Model - Stack Overflow

WebUse Python(Numpy, Scikit-learn, Pandas) for combining different files and process automation. ... Linear Regression, Decision Tree, Prediction Accuracy Validation, Optimization, Deep Learning, k ... WebNow we can create the actual decision tree, fit it with our details. Start by importing the modules we need: Example Get your own Python Server. Create and display a Decision Tree: import pandas. from sklearn import … WebNow we can create the actual decision tree, fit it with our details. Start by importing the modules we need: Example Get your own Python Server Create and display a Decision Tree: import pandas from sklearn import … climbing stairs knee problems