Model evaluation is a crucial step in the process of developing a stock price prediction model. In this tutorial, we will discuss various evaluation metrics and techniques to assess the performance of your model. 📈

Evaluation Metrics

Here are some commonly used evaluation metrics for regression models:

  • Mean Absolute Error (MAE): This metric calculates the average absolute difference between the predicted and actual values.
  • Mean Squared Error (MSE): Similar to MAE, but squares the differences before averaging. This metric gives more weight to larger errors.
  • Root Mean Squared Error (RMSE): The square root of MSE, providing a more intuitive measure of error.
  • R-squared: Also known as the coefficient of determination, this metric indicates the proportion of variance in the dependent variable that is predictable from the independent variable(s).

Techniques for Model Evaluation

To evaluate your model, you can use the following techniques:

  • Cross-validation: This technique involves splitting the dataset into k subsets. For each subset, the model is trained on k-1 subsets and evaluated on the remaining subset. This process is repeated k times, with each subset serving as the validation set once.
  • Resampling: This technique involves repeatedly training and evaluating the model on different subsets of the data.
  • Holdout: This technique involves splitting the dataset into a training set and a validation set. The model is trained on the training set and evaluated on the validation set.

Example

Let's say you have a dataset containing historical stock prices and you want to predict future prices. You can use the following code to evaluate your model using cross-validation:

from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LinearRegression

# Load the dataset
data = ...

# Initialize the model
model = LinearRegression()

# Evaluate the model using cross-validation
scores = cross_val_score(model, data.X, data.y, cv=5)

# Print the average score
print("Average score:", scores.mean())

Further Reading

For more information on model evaluation, you can refer to the following resources:

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