Interview for Machine Learning

Tag: Fit

  • ML0040 Bias and Variance

    Can you explain the bias-variance tradeoff?

    Answer

    Bias:
    Error due to overly simplified assumptions in the model.
    High bias may lead to underfitting, where the model misses key patterns in the data.

    Variance:
    Error due to high sensitivity to variations in the training data.
    High variance may result in overfitting, where the model captures noise and underlying patterns.

    Bias-Variance Tradeoff:
    Increasing model complexity typically decreases bias but increases variance, while a simpler model increases bias but decreases variance.
    The goal is to balance both to minimize the total error on unseen data.

    The bias-variance tradeoff illustrates that there’s a delicate balance to strike when building a machine learning model. A simpler model tends to have high bias and low variance, underfitting the data. A more complex model tends to have low bias and high variance, overfitting the data. The goal is to find the right level of model complexity to minimize the total prediction error, which is the sum of squared bias, variance, and irreducible error.

    The example below shows scenarios of high bias (underfitting), high variance (overfitting), and a good balance.

    Login to view more content

  • ML0004 Underfitting

    Which of the following descriptions is inaccurate in regard to underfitting?

    A. Underfitting occurs when a model is too simple to capture the underlying patterns from the data.

    B. When underfitting occurs, the model will have high bias and low variance.

    C. Increasing the model’s complexity and reducing regularization can address underfitting.

    D. An underfit model performs well with the training data but performs poorly on new, unseen data.

    Answer

    D
    Explanation:
    Underfitting means the model performs poorly on both the training data and the unseen test data because it hasn’t learned enough from the training set.

    Login to view more content

  • ML0003 Overfitting

    What is overfitting and how to avoid overfitting?

    Answer

    Overfitting happens when a model tries to learn the training data too well, including its noise and outliers, leading to poor performance on new, unseen data. The model becomes too specialized to the training data, failing to generalize to other data.

    To avoid overfitting:
    1. Simplify the model: Use less complex models.
    2. Get more data or use data agumentation: A larger dataset helps the model generalize.
    3. Regularization: Penalize complex models with techniques like L1/L2 regularization.
    4. Validation & Early Stopping: Validate frequently and stop training when performance plateaus.
    5. For neural networks, the dropout layer can also be used to avoid overfitting.

    Login to view more content