Oversample minority classes

competition/random_forest.py

@@ -1,6 +1,7 @@
 import numpy as np
 import pandas as pd
 import pickle
+from imblearn.over_sampling import SMOTE
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.metrics import classification_report, confusion_matrix
 from sklearn.model_selection import train_test_split
@@ -22,7 +23,10 @@ y = np.array(df['sublabel'])
 
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y)
 
-clf = RandomForestClassifier(n_estimators=50, n_jobs=-1, criterion='gini', random_state=0)
+oversampler = SMOTE(random_state=777, k_neighbors=5)
+X_train, y_train = oversampler.fit_resample(X_train, y_train)
+
+clf = RandomForestClassifier(n_estimators=20, n_jobs=-1, criterion='gini', random_state=0)
 clf.fit(X_train, y_train)
 
 accuracy = clf.score(X_test, y_test)

competition/requirements.txt

@@ -13,3 +13,4 @@ scipy==1.6.3
 six==1.16.0
 sklearn==0.0
 threadpoolctl==2.1.0
+imbalanced-learn==0.8.0