Day 6: Training a Sentiment Analysis Model
Welcome back to our sentiment analysis project! Now that we’ve explored different machine learning models and selected the best one for our task, it’s time to train our model using the feature matrices we engineered in the previous blog post. In today’s post, we’ll walk through the process of splitting our dataset into training and testing sets, training the selected machine learning model, and evaluating its performance.
Splitting the Dataset
Before we train our model, we need to split our dataset into two subsets: a training set and a testing set. The training set will be used to train the model, while the testing set will be used to evaluate its performance. Typically, the dataset is split into approximately 70-80% for training and 20-30% for testing. We’ll use the train_test_split function from scikit-learn to split our dataset:
from sklearn.model_selection import train_test_split
# Split the dataset into training and testing sets (80% train, 20% test)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
# Display the shapes of the training and testing sets
print("Training set shape:", X_train.shape, y_train.shape)
print("Testing set shape:", X_test.shape, y_test.shape)Training the Model
With our dataset split into training and testing sets, we can now train our selected machine learning model using the training data. We’ll fit the model to the training set and learn the underlying patterns in the data:
from sklearn.linear_model import LogisticRegression
# Initialize the logistic regression model
model = LogisticRegression()
# Train the model on the training set
model.fit(X_train, y_train)Evaluating Model Performance
Once the model is trained, we need to evaluate its performance on the testing set to assess how well it generalizes to unseen data. We’ll use metrics such as accuracy, precision, recall, and F1-score to evaluate the model’s performance:
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
# Make predictions on the testing set
y_pred = model.predict(X_test)
# Calculate evaluation metrics
accuracy = accuracy_score(y_test, y_pred)
precision = precision_score(y_test, y_pred, average='weighted')
recall = recall_score(y_test, y_pred, average='weighted')
f1 = f1_score(y_test, y_pred, average='weighted')
# Print the evaluation metrics
print("Accuracy:", accuracy)
print("Precision:", precision)
print("Recall:", recall)
print("F1-score:", f1)Conclusion
In this blog post, we’ve trained our selected machine learning model for sentiment analysis using the feature matrices we engineered in the previous post. We split the dataset into training and testing sets, trained the model on the training data, and evaluated its performance on the testing data using metrics such as accuracy, precision, recall, and F1-score.
Stay tuned for tomorrow’s post, where we’ll dive deeper into fine-tuning the model to improve its performance further.
If you have any questions or thoughts, feel free to share them in the comments section below!
