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This commit is contained in:
ShaaniBel 2025-09-28 23:40:35 -04:00
parent 5d1e5e75c2
commit f261b06dff
2 changed files with 17 additions and 9 deletions
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logistic-regression-wdbc.py
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@ -91,7 +91,7 @@ class LogisticRegression:
print(f"Converged after {i} iterations.")
break # loss is stopped so further training would be unnecessary
def predict(self, x: np.ndarray | pd.DataFrame) -> np.ndarray:
def predict(self, x: pd.DataFrame) -> np.ndarray:
"""
Predict method is used to test trained data to do Y prediction by multiplying X and weight vectors
and then calculates the model probability by applying sigmoid function.
@ -104,7 +104,7 @@ class LogisticRegression:
probs = self.sigmoid(z) # probability calculation through sigmoid method
return (probs >= 0.5).astype(int) # 0.5 is commonly used to define positivity of the probability
def score(self, x: np.ndarray | pd.DataFrame, y: np.ndarray | pd.Series) -> float:
def score(self, x: pd.DataFrame, y: pd.Series) -> float:
"""
This method is used to calculate mean accuracy with the prediction of Y and actual Y values.
"""
@ -112,7 +112,7 @@ class LogisticRegression:
y_true = np.asarray(y).astype(int)
return np.mean(y_pred == y_true) # mean is calculated if Y values match
def confusion_matrix(self, x: np.ndarray | pd.DataFrame, y: np.ndarray | pd.Series,
def confusion_matrix(self, x: pd.DataFrame, y: pd.Series,
normalize: bool = False) -> np.ndarray:
"""
Confusion Matrix
@ -128,7 +128,7 @@ class LogisticRegression:
return cm
def plot_confusion_matrix(self, x: np.ndarray | pd.DataFrame, y: np.ndarray | pd.Series,
def plot_confusion_matrix(self, x: pd.DataFrame, y: pd.Series,
normalize: bool = False, title: str = "Confusion Matrix", sns=None) -> None:
"""
Plot confusion matrix as a heatmap
@ -145,7 +145,7 @@ class LogisticRegression:
plt.xlabel('Predicted Label')
plt.show()
def precision(self, x: np.ndarray | pd.DataFrame, y: np.ndarray | pd.Series) -> float:
def precision(self, x: pd.DataFrame, y: pd.Series) -> float:
"""
Precision = TP / (TP + FP)
Measures how many of the predicted positives are actually positive
@ -158,7 +158,7 @@ class LogisticRegression:
return tp / (tp + fp)
def recall(self, x: np.ndarray | pd.DataFrame, y: np.ndarray | pd.Series) -> float:
def recall(self, x: pd.DataFrame, y: pd.Series) -> float:
"""
Recall = TP / (TP + FN)
ratio of true positives to all the positives in ground truth