I have read in a csv file which contains 8 predictive features ( col_list
) and one target feature (Target variable is called " chd
" -> 1= Heart Attack; 0 = No Heart Attack).
df = pd.read_csv(loc+'HeartDisease.csv', index_col=0)
Y = df['chd']
col_list = ['sbp','tobacco','ldl','adiposity','typea','obesity','alcohol','age']
I have trained an XGBoost Classifier:
# fit model no training data
model = XGBClassifier(
base_score=0.1,
booster='gbtree',
colsample_bylevel=1,
colsample_bynode=1,
colsample_bytree=0.6,
enable_categorical=False,
gamma=0.1,
gpu_id=-1,
importance_type=None,
interaction_constraints='',
learning_rate=0.1,
max_delta_step=0,
max_depth=8,
min_child_weight=1,
monotone_constraints='(1,1,1,1,1,1,1,1)',#,"(1,-1)"
n_estimators=4, n_jobs=1,
nthread=1,
num_parallel_tree=1,
predictor='auto',
random_state=0,
reg_alpha=0,
reg_lambda=1,
scale_pos_weight=1,
silent=True,
subsample=0.6,
tree_method='exact',
validate_parameters=1,
verbosity=None)
I have then visualized the tree:
fig, ax = plt.subplots(figsize=(30, 30))
plot_tree(model,ax=ax)
plt.show()
How can I create a column called " leaf
" in the df
dataframe that contains the values of the terminal leaves shown in the picture above?
You can use xgboost.Booster
's method trees_to_dataframe
:
df = model.Booster.trees_to_dataframe()
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