What is underfitting, and how does it differ from overfitting?
- Enhancing model complexity; similar to overfitting
- Fitting the model too closely to the training data; same as overfitting
- Fitting the model too loosely to the training data; opposite of overfitting
- Reducing model complexity; similar to overfitting
Underfitting is when a model fits the training data too loosely and fails to capture the underlying pattern, the opposite of overfitting, where the model fits too closely.
In the context of Decision Trees, how can overfitting be controlled using pruning techniques?
- By increasing the number of features
- By increasing the tree complexity
- By reducing the training data
- By reducing the tree complexity
Overfitting in Decision Trees can be controlled using pruning techniques by reducing the tree's complexity. By removing branches that add little predictive power, the model becomes less sensitive to noise in the training data and generalizes better to unseen examples.
In classification, when a model is biased toward predicting one class over another, it is known as a(n) ________ problem.
- Clustering
- Imbalanced classification
- Multiclass classification
- Overfitting
When a model consistently predicts one class over another, particularly when the classes are not equally represented, this is known as an imbalanced classification problem.
You notice that a Decision Tree is providing inconsistent results on different runs. How might you investigate and correct the underlying issue, possibly involving entropy, Gini Index, or pruning techniques?
- Analyze the randomness in splitting and apply consistent pruning techniques
- Change to a different algorithm
- Ignore inconsistent results
- Increase tree depth
Inconsistent results may stem from the randomness in splitting the data. Analyzing this aspect and applying consistent pruning techniques can help create more stable, reproducible results. Attention to the splitting criteria, such as entropy or Gini Index, can further refine the model's behavior.
What is the primary goal of clustering algorithms?
- To classify labeled data
- To find patterns and group similar data together
- To predict outcomes
- To solve reinforcement learning problems
The primary goal of clustering algorithms is to find patterns in the data and group similar data points together without using any labeled responses.
The ___________ matrix in LDA represents the spread of the individual classes around their mean.
- between-class scatter
- correlation
- covariance
- within-class scatter
The "within-class scatter" matrix in LDA represents the spread of the individual classes around their mean, capturing the within-class variance.
Explain how cross-validation can be used to mitigate the risk of overfitting.
- By adding noise to the training data
- By increasing model complexity
- By reducing model complexity
- By splitting the data into multiple subsets and training on different combinations
Cross-validation mitigates the risk of overfitting "by splitting the data into multiple subsets and training on different combinations." It ensures that the model is evaluated on unseen data and helps in tuning hyperparameters without relying on the test set.
____________ Learning, a subset of Machine Learning, is essential in training robots to perform specific tasks in manufacturing industries.
- Disease Prediction
- Fraud Detection
- Reinforcement
- Weather Prediction
Reinforcement Learning, a subset of Machine Learning, plays a crucial role in training robots for specific tasks in manufacturing through trial and error and receiving feedback from the environment.
What is the primary purpose of using regularization techniques in Machine Learning models?
- Enhance data visualization
- Increase accuracy
- Increase model complexity
- Reduce overfitting
Regularization techniques are used to prevent overfitting by adding constraints to the model, thus helping it to generalize better on unseen data.
ElasticNet is a hybrid regularization technique that combines the L1 penalty of ________ and the L2 penalty of ________.
- ElasticNet, Ridge
- Lasso, Ridge
- Ridge, Lasso
- nan
ElasticNet combines the L1 penalty of Lasso and the L2 penalty of Ridge, providing a middle ground between the two techniques.