<jupyter_start><jupyter_text>Our model accuracy is 97, 77 %.<jupyter_code>#Let's see which features are important

#Create object with feature names
feature_cols = iris_df.columns[1:5]
feature_cols

feature_importance = pd.Series(classifier.feature_importances_,index= feature_cols).sort_values(ascending=True)
feature_importance

#Visualise Feature importance 
sns.barplot(x=feature_importance ,y=feature_cols)
# Add labels to your graph
plt.xlabel('Feature Importance Score')
plt.ylabel('Features')
plt.title("Important Features Visualization")<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.0954861111111111, 'fraction_numerical': 0.010416666666666666, 'mean_word_length': 9.12280701754386, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '254137', 'license': 'MIT', 'n_tokens_mistral': 192, 'n_tokens_neox': 183, 'n_words': 41, 'path': '4. Decision Tree & Random Forest Classification/Random Forest Classification.ipynb', 'repo_name': 'bbp-earthling/Machine-Learning-With-Python-Scikit-Learn', 'size': '576'}