title: How do I optimize my random forest model in scikit Learn (Python)
body: <p>I have a random forest model using scikit learn, as seen here:</p>
  <pre><code>model = RandomForestClassifier(criterion='gini',n_estimators=700,min_samples_split=4,min_samples_leaf=1,max_features='auto',oob_score=True,random_state=1,n_jobs=-1)
  model.fit(X_train,y_train)
  y_pred_rm=model.predict(X_test)
  print('--------------The Accuracy of the model---------------------------')
  kfold = KFold(n_splits=10, random_state=22)
  result_rm = cross_val_score(model, all_features, Targeted_feature, cv=10, scoring = 'accuracy')
  print('The cross validated score for Random Forest Classifier is:',round(result_rm.mean()*100,2))
  y_pred = cross_val_predict(model,all_features,Targeted_feature,cv=10)
  kfold = KFold(n_splits=5, random_state=22)
  result_rm1 = cross_val_score(model, all_features, Targeted_feature, cv=5, scoring='accuracy')
  print('The cross validated score (5)for Random Forest Classifier is:',round(result_rm1.mean()*100,2))
  sns.heatmap(confusion_matrix(Targeted_feature,y_pred),annot=True,fmt='3.0f',cmap=&quot;winter&quot;)
  plt.title('Confusion_matrix', y=1.05, size=15)
  </code></pre>
  <p>I've been trying to optimize my model, but haven't had any success yet. The highest accuracy rate I have achieved on the test dataset is 78%. Do you have any ideas or steps I could take to improve my model?</p>