Breaking News: Grepper is joining You.com. Read the official announcement!

decision tree algorithm python

Add Answer

Athul Mathew answered on May 9, 2022 Popularity 10/10 Helpfulness 5/10

answer decision tree algorithm python

related decision tree python

related python decision tree

decision tree algorithm python

Comment

import pandas
from sklearn import tree
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt

df = pandas.read_csv("data.csv")

d = {'UK': 0, 'USA': 1, 'N': 2}
df['Nationality'] = df['Nationality'].map(d)
d = {'YES': 1, 'NO': 0}
df['Go'] = df['Go'].map(d)

features = ['Age', 'Experience', 'Rank', 'Nationality']

X = df[features]
y = df['Go']

dtree = DecisionTreeClassifier()
dtree = dtree.fit(X, y)

tree.plot_tree(dtree, feature_names=features)

Popularity 10/10 Helpfulness 5/10 Language python

Source: Grepper

Tags: algorithm decision-tree python

Link to this answer
Share Copy Link

Contributed on Aug 18 2022

Athul Mathew

0 Answers Avg Quality 2/10

Closely Related Answers

decision tree python

Comment

# Split into train and test set
from sklearn.model_selection import train_test_split
X_Train, X_Test, y_Train, y_Test = train_test_split(X, y, test_size=0.3, random_state=3)

# Make sure to take into account the class imbalance 
from sklearn.utils.class_weight import compute_sample_weight
w_train = compute_sample_weight('balanced', y_train)

# Train the classifier
from sklearn.tree import DecisionTreeClassifier
tree_clf = DecisionTreeClassifier(criterion="entropy", max_depth = 4)
tree_clf.fit(X_Train,y_Train, sample_weight=w_train)

# Alternative approach : Train the classifier with snapml (offers multi-threaded CPU/GPU training)
from snapml import DecisionTreeClassifier
snapml_dt_gpu = DecisionTreeClassifier(max_depth=4, random_state=45, use_gpu=True)
snapml_dt_cpu = DecisionTreeClassifier(max_depth=4, random_state=45, n_jobs=4)
snapml_dt.fit(X_train, y_train, sample_weight=w_train)
# Predict
y_pred = tree_clf.predict(X_Test)

### Inspecting a random forest
# Pull out one tree from the forest (If decision tree is a random forest)
chosen_tree = randomforest_model.estimators_[7] # You can visualize it with (graphviz & pydotplus)
# Extract node decisions
split_column = chosen_tree.tree_.feature[0] # Get the first column it split on
split_column_name = X_train.columns[split_column] # Name of the column
split_value = chosen_tree.tree_.threshold[1] # Get the theshold value it split on

# Compute predicted probabilities
y_pred_prob = tree_clf.predict_proba(X_test)[:,1]

# Evaluate tree
from sklearn.metrics import roc_auc_score, accuracy_score
accuracy_score(y_testset, predTree)
roc_auc_score(y_test, y_pred)

# Visualize the graph using plot_tree
from sklearn.tree import plot_tree
plt.figure(figsize=(20, 10))
plot_tree(chosen_tree, feature_names=X_train.columns, filled=True, rounded=True, fontsize=10)
plt.show()

Popularity 9/10 Helpfulness 5/10 Language python

Source: Grepper

Tags: decision-tree de

Link to this answer
Share Copy Link

Contributed on Feb 12 2023

Innocent Iguana

0 Answers Avg Quality 2/10

python decision tree

Comment

# 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))

Popularity 10/10 Helpfulness 2/10 Language python

Source: www.datacamp.com

Tags: decision-tree de

Link to this answer
Share Copy Link

Contributed on May 09 2022

Ruben Visser

0 Answers Avg Quality 2/10

decision tree algorithm python

Contents

More Related Answers

decision tree algorithm python

Closely Related Answers

decision tree python

python decision tree

Grepper

Documentation

Social

Legal

Contact

Oops, You will need to install Grepper and log-in to perform this action.