random_state

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1) # Train the model model = RandomForestClassifier(n_estimators=100, random_state=1) model.fit(X_train, y_train) ``` #### Step 2: Pre-Fetching Logic I…

Here's an example of how you might set up the grid search and logging: ```python from sklearn.model_selection import train_test_split from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score import logging # Exa…

### Step 2: Modify the Code to Use BM25 Here's an example of how you can integrate BM25 into your proof of concept: ```python import pandas as pd from sklearn.model_selection import train_test_split from sklearn.metrics import recall_scor…

# Test the model y_pred = model.predict(X_test_scaled) accuracy = accuracy_score(y_test, y_pred) logger.info(f"Test Accuracy: {accuracy:.2f}") return model, accuracy # Example data features = np.random.rand(18000, …

```python import numpy as np from sklearn.metrics import accuracy_score from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split import redis import logging # Set up logging configuration log…

- Review the logs and debugging output to identify the root cause of the issue. ### Example Implementation Let's assume you have an evaluation pipeline that uses Scikit-learn for model evaluation. We'll add detailed logging and use `pd…

logging.basicConfig(filename='evaluation_pipeline.log', level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s') # Load dataset X, y = np.random.rand(10000, 10), np.random.randint(0, 2, 10000) # Split t…

2. **Accuracy Score**: This is a metric from `sklearn.metrics` that computes the accuracy of the model's predictions. It is the ratio of the number of correct predictions to the total number of predictions. 3. **Cross-validation Function**…

X = np.random.rand(11000, 10) y = np.random.randint(0, 2, size=11000) # Split data X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Define pipeline pipeline = Pipeline([ ('scaler', StandardSc…

### Step 4: Modify Your Script for Logging Ensure your Python script logs the metrics to a file named `metrics.log`. Here's an updated version of the script: ```python import numpy as np from sklearn.datasets import make_classification fr…