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

Visualize Decision Tree

Elisabeth Engering answered on July 4, 2022 Popularity 9/10 Helpfulness 4/10

answer Visualize Decision Tree

related visualize decision tree python

Visualize Decision Tree

Comment

# Visualise the Decision Tree 
plt.figure(figsize=(35,40)) # Set plot size (denoted in inches)
tree.plot_tree(model, # Create plot
               fontsize = 10) # Set fontsize
plt.show() # Display plot

Popularity 9/10 Helpfulness 4/10 Language python

Source: Grepper

Tags: decision-tree python visualize

Link to this answer
Share Copy Link

Contributed on Jul 04 2022

Elisabeth Engering

0 Answers Avg Quality 2/10

Closely Related Answers

visualize 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 6/10 Language python

Source: Grepper

Tags: decision-tree de

Link to this answer
Share Copy Link

Contributed on Jan 20 2024

Innocent Iguana

0 Answers Avg Quality 2/10

Visualize Decision Tree

Contents

More Related Answers

Visualize Decision Tree

Closely Related Answers

visualize decision tree python

Grepper

Documentation

Social

Legal

Contact

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