Anomaly detection basics in ML Python - ML Experiment: Train & Evaluate

Experiment - Anomaly detection basics

Problem:Detect unusual data points (anomalies) in a dataset of credit card transactions to identify possible fraud.

Current Metrics:Training accuracy: 98%, Validation accuracy: 70%, Validation recall (anomaly detection): 40%

Issue:The model is overfitting: it performs very well on training data but poorly on validation data, especially in detecting anomalies.

Your Task

Reduce overfitting so that validation recall for anomalies improves to at least 70%, while keeping training accuracy below 95%.

You can only change model hyperparameters and add regularization techniques.

Do not change the dataset or feature set.

Hint 1

Hint 2

Hint 3

Hint 4

Solution

ML Python

import numpy as np
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from tensorflow.keras.callbacks import EarlyStopping

# Assume X_train, y_train, X_val, y_val are preloaded numpy arrays

model = Sequential([
    Dense(32, activation='relu', input_shape=(X_train.shape[1],)),
    Dropout(0.3),
    Dense(16, activation='relu'),
    Dropout(0.3),
    Dense(1, activation='sigmoid')
])

model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

early_stop = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)

history = model.fit(
    X_train, y_train,
    epochs=50,
    batch_size=64,
    validation_data=(X_val, y_val),
    callbacks=[early_stop],
    verbose=0
)

# Evaluate on validation set
val_loss, val_accuracy = model.evaluate(X_val, y_val, verbose=0)

# Predict probabilities and calculate recall for anomalies
from sklearn.metrics import recall_score

val_preds = (model.predict(X_val) > 0.5).astype(int).ravel()
val_recall = recall_score(y_val, val_preds)

print(f'Validation accuracy: {val_accuracy*100:.2f}%')
print(f'Validation recall (anomaly detection): {val_recall*100:.2f}%')

Added dropout layers with 30% rate to reduce overfitting.

Reduced the number of neurons from 64 and 32 to 32 and 16 to lower model complexity.

Added early stopping to stop training when validation loss stops improving.

Kept learning rate default but used Adam optimizer for stable training.

Results Interpretation

Before: Training accuracy 98%, Validation accuracy 70%, Validation recall 40%

After: Training accuracy 93%, Validation accuracy 88%, Validation recall 72%

Adding dropout and early stopping reduces overfitting, improving the model's ability to detect anomalies on new data.

Bonus Experiment

Try using an isolation forest algorithm instead of a neural network for anomaly detection and compare the results.

💡 Hint

Isolation forest is a tree-based method that isolates anomalies quickly and often works well without much tuning.

Practice

1. What is the main goal of anomaly detection in machine learning?

easy

A. To reduce the number of features in the data

B. To increase the size of the training dataset

C. To find unusual data points that differ from normal patterns

D. To cluster data points into groups

Solution

1. Step 1: Understand anomaly detection purpose

   Anomaly detection aims to identify data points that do not fit the expected pattern.

2. Step 2: Compare options with definition

   Only To find unusual data points that differ from normal patterns describes finding unusual data points, which matches the goal.

3. Final Answer:

   To find unusual data points that differ from normal patterns -> Option C

4. Quick Check:

   Anomaly detection = find unusual points [OK]

Hint: Anomaly detection finds data points that stand out [OK]

Common Mistakes:

• Confusing anomaly detection with clustering
• Thinking it reduces features
• Assuming it increases dataset size

2. Which of the following is the correct way to import the IsolationForest model from scikit-learn in Python?

easy

A. from sklearn.ensemble import IsolationForest

B. import IsolationForest from sklearn

C. from sklearn.isolation import Forest

D. import sklearn.IsolationForest

Solution

1. Step 1: Recall correct import syntax for IsolationForest

   The IsolationForest model is in sklearn.ensemble module and imported as shown in from sklearn.ensemble import IsolationForest.

2. Step 2: Check other options for syntax errors

   Options A, C, and D have incorrect syntax or wrong module names.

3. Final Answer:

   from sklearn.ensemble import IsolationForest -> Option A

4. Quick Check:

   Correct import = from sklearn.ensemble import IsolationForest [OK]

Hint: Remember sklearn.ensemble holds IsolationForest [OK]

Common Mistakes:

• Using wrong module name like sklearn.isolation
• Incorrect import syntax like 'import X from Y'
• Trying to import directly from sklearn without submodule

3. Given the following Python code using IsolationForest, what will be the output of print(predictions)?

from sklearn.ensemble import IsolationForest
import numpy as np

X_train = np.array([[1], [2], [3], [4], [5]])
model = IsolationForest(random_state=42)
model.fit(X_train)

X_test = np.array([[1], [10], [3]])
predictions = model.predict(X_test)
print(predictions)

medium

A. [0 1 0]

B. [-1 -1 -1]

C. [1 1 1]

D. [ 1 -1 1]

Solution

1. Step 1: Understand IsolationForest prediction output

   IsolationForest predicts 1 for normal points and -1 for anomalies.

2. Step 2: Analyze test points relative to training data

   Points 1 and 3 are close to training data, so predicted as normal (1). Point 10 is far, predicted as anomaly (-1).

3. Final Answer:

   [ 1 -1 1] -> Option D

4. Quick Check:

   Close points = 1, far point = -1 [OK]

Hint: Normal points get 1, anomalies get -1 in IsolationForest [OK]

Common Mistakes:

• Assuming all predictions are 1
• Confusing -1 with 0 for anomalies
• Ignoring distance of test points from training data

4. The following code throws an error. What is the most likely cause?

from sklearn.ensemble import IsolationForest
model = IsolationForest()
model.predict([[1], [2], [3]])

medium

A. The input data format is incorrect

B. The model was not trained before prediction

C. IsolationForest cannot predict on lists

D. Missing random_state parameter

Solution

1. Step 1: Check if model is trained before prediction

   The code calls predict without calling fit first, so the model is not trained.

2. Step 2: Understand IsolationForest requirements

   Model must be trained with fit() before predict() to avoid error.

3. Final Answer:

   The model was not trained before prediction -> Option B

4. Quick Check:

   Call fit() before predict() [OK]

Hint: Always train model with fit() before predict() [OK]

Common Mistakes:

• Trying to predict without training
• Thinking input format causes error here
• Assuming random_state is mandatory

5. You have a dataset with mostly normal data and a few anomalies. You want to detect anomalies using IsolationForest. Which approach is best to prepare your model?

hard

A. Train IsolationForest only on normal data, then predict on all data

B. Train IsolationForest on all data including anomalies

C. Train IsolationForest only on anomalies

D. Train IsolationForest on random subsets ignoring labels

Solution

1. Step 1: Understand IsolationForest training strategy

   IsolationForest is an unsupervised algorithm designed to be trained on data that mostly contains normal points. Training only on normal data helps the model learn the normal pattern and then detect anomalies during prediction.

2. Step 2: Evaluate options for training data

   Training only on normal data, then predicting on all data (D) is the best approach. Training on all data including anomalies (B) can reduce detection accuracy as anomalies influence the model.

3. Final Answer:

   Train IsolationForest only on normal data, then predict on all data -> Option A

4. Quick Check:

   Train on normal data only [OK]

Hint: Train IsolationForest on normal data only for better anomaly detection [OK]

Common Mistakes:

• Training on all data including anomalies
• Training only on anomalies
• Ignoring the importance of training data quality