I have a dataset which has 450.000 data points, 12 features and label(0 or 1). I am using imblearn library of python because my dataset is imbalanced(ratio= 1:50, class 1 is minority). I am using EasyEnsembleClassifier as classifier. My problem is; I get high recall but very low precision as you can see from image below(90% recall, 8% precision, 14% f1 score).
Here is my code:
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from dask_ml.preprocessing import RobustScaler
from imblearn.ensemble import EasyEnsembleClassifier
from sklearn.metrics import classification_report, f1_score, accuracy_score, precision_score, confusion_matrix
from sklearn import metrics
df = read_csv(...)
X = df[['features...']]
y = df['label']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = RobustScaler()
X_train = scaler.fit_transform(X_train)
clf = EasyEnsembleClassifier(n_estimators=50, n_jobs=-1, sampling_strategy = 1.0)
clf.fit(X_train, y_train)
X_test = scaler.transform(X_test)
y_pred = clf.predict(X_test)
------code for report------
.............
Output:
I tried different scalers namely MinMaxScaler, StandardScaler. I tried changing test-train split ratio, different parameters of EasyEnsembleClassifier. I also tried BalancedRandomForestClassifier from same library but result are same. Changing number of estimators in classifier parameter also doesn't change the result.
What is the reason of this results? What can I do to improve precision without damaging recall? It looks like I am doing something wrong in my code or I am missing an important concept.
Edit: I still couldn't figure out the true reason of my problem but since no one answered my question here is some ideas about what could be the reason of this weird model in case someone else encounters with similar problem;
You could try other ensemble methods for class imbalance learning. SMOTEBoost is one such method that combines boosting and data sampling method, essential injects SMOTE technique at each boosting iteration.
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