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Visualize Decision Tree

Elisabeth Engering answered on July 4, 2022 Popularity 9/10 Helpfulness 5/10

answer Visualize Decision Tree

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Visualize Decision Tree

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# 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 5/10 Language python

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Tags: decision-tree python visualize

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Contributed on Jul 04 2022

Elisabeth Engering

0 Answers Avg Quality 2/10

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visualize decision tree python

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# 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 7/10 Helpfulness 7/10 Language python

Source: Grepper

Tags: decision-tree de

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Contributed on Jan 20 2024

Innocent Iguana

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Visualize Decision Tree

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