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we are importing from sklearn.neural_network import MLPClassifier # # Create a multi-layer perceptron classifier model = MLPClassifier() # # Train the model model.fit(X_train, y_train) y_pred =model.predict(X_test) cm = confusion_matrix(y_test,y_pred) cm print("confusion_matrix:",confusion_matrix(y_test,y_pred)) print("Precision_score:",precision_score(y_test,y_pred)) print("Recall_score:",recall_score(y_test,y_pred)) print("F1 score:",f1_score(y_test,y_pred))
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from sklearn.model_selection import cross_val_score
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.naive_bayes import GaussianNB
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor# Initialize models
models = {
"Logistic Regression": LogisticRegression(),
"K Nearest Neighbors": KNeighborsClassifier(),
"Support Vector Machines": SVC(),
"Kernel SVM": SVC(kernel='rbf'),
"Naive Bayes": GaussianNB(),
"Decision Tree Classification": DecisionTreeClassifier(),
"Decision Tree Regression": DecisionTreeRegressor()
}# Load your data and split into features X and target variable y# Define evaluation metrics
scoring = {
'accuracy': 'accuracy',
'precision': 'precision_macro',
'recall': 'recall_macro',
'f1': 'f1_macro'
}# Perform model selection
results = {}
for name, model in models.items():
scores = cross_val_score(model, X, y, cv=5, scoring=scoring)
results[name] = {
'Accuracy': scores.mean(),
'Precision': scores.mean(),
'Recall': scores.mean(),
'F1': scores.mean()
}# Print results
for name, scores in results.items():
print(f"Model: {name}")
print(f"Accuracy: {scores['Accuracy']:.4f}")
print(f"Precision: {scores['Precision']:.4f}")
print(f"Recall: {scores['Recall']:.4f}")
print(f"F1: {scores['F1']:.4f}")
print()
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from sklearn.ensemble import GradientBoostingClassifier# Create a gradient boosting classifier
model = GradientBoostingClassifier()# # Train the model
model.fit(X_train, y_train)
y_pred =model.predict(X_test)
cm = confusion_matrix(y_test,y_pred)
cmprint("confusion_matrix:",confusion_matrix(y_test,y_pred))
print("Precision_score:",precision_score(y_test,y_pred))
print("Recall_score:",recall_score(y_test,y_pred))
print("F1 score:",f1_score(y_test,y_pred))
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from sklearn.neural_network import MLPClassifier# # Create a multi-layer perceptron classifier
model = MLPClassifier()# # Train the model
model.fit(X_train, y_train)
y_pred =model.predict(X_test)
cm = confusion_matrix(y_test,y_pred)
cmprint("confusion_matrix:",confusion_matrix(y_test,y_pred))
print("Precision_score:",precision_score(y_test,y_pred))
print("Recall_score:",recall_score(y_test,y_pred))
print("F1 score:",f1_score(y_test,y_pred))
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score# Assuming you have a dataset with features X and corresponding labels y
# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)# Create a Random Forest classifier object
rf = RandomForestClassifier(n_estimators=100, random_state=42)# Train the classifier on the training data
rf.fit(X_train, y_train)# Make predictions on the test data
y_pred = rf.predict(X_test)# Evaluate the accuracy of the classifier
accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
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Everything is clear. Alhamdulillah
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Sir,to day lecturer us difficult.
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currently looking difficult, hope so inshaALLAH after 1st oct it will be easy to understand when attempt it 2nd time.
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