Implement Random Forest Classifier model
diff --git a/mlscorecard/models.py b/mlscorecard/models.py
index bb9265f..0eef237 100644
--- a/mlscorecard/models.py
+++ b/mlscorecard/models.py
@@ -21,6 +21,7 @@
assessment
"""
+import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
@@ -28,8 +29,7 @@
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV, ShuffleSplit
-from sklearn.metrics import confusion_matrix, accuracy_score, f1_score
-
+from sklearn.metrics import confusion_matrix, accuracy_score, f1_score, recall_score, precision_score
class Model(object):
"""
@@ -68,6 +68,8 @@
Random State: {self.random_state}
Number of Splits: {self.n_splits}
Parameter Grid: {self.params}
+
+ {self.model}
"""
def train(self, x_train, y_train):
@@ -121,9 +123,14 @@
y_pred = self.predict(x_test)
cm = confusion_matrix(y_pred, y_test)
- acc_sc = accuracy_score(y_test, y_pred, normalize=True)
- f1 = f1_score(self.y_test, y_pred, average=None)
- return {"accuracy" : acc_sc, "f1_score" : f1, "confusion_matrix" : cm}
+ accuracy = accuracy_score(y_test, y_pred, normalize=True)
+ f1 = f1_score(self.y_test, y_pred, average="macro")
+ recall = recall_score(y_test, y_pred, average="macro")
+ precision = precision_score(y_test, y_pred, average="macro")
+ return {"accuracy" : accuracy,
+ "f1_score" : f1,
+ "recall_score" : recall,
+ "precision_score": precision}
class RandomForest(Model):
@@ -184,6 +191,10 @@
for category in categorical:
data[category] = le.fit_transform(data[category])
+ for col in data.columns:
+ if(col not in categorical):
+ data[col] = (data[col].astype('float') - np.mean(data[col].astype('float')))/np.std(data[col].astype('float'))
+
# Get Training parameters
target_col = data.columns[-1]
x = data.drop(columns=target_col, axis=1)
