Why might it be important to consider interaction effects in a Multiple Linear Regression model?
- It captures complex relationships
- It increases accuracy independently
- It reduces bias
- It simplifies the model
Considering interaction effects is essential to capture complex relationships between variables that might not be apparent when considering each variable separately.
Describe how Machine Learning algorithms are implemented in sentiment analysis and customer feedback systems.
- Drug Discovery
- Image Recognition
- Inventory Management
- Text Classification
Sentiment analysis in customer feedback systems often involves text classification techniques. Machine learning algorithms like SVM, Naïve Bayes, or deep learning models can categorize customer comments into positive, negative, or neutral sentiment.
When applying the K-Nearest Neighbors algorithm, scaling the features is essential because it ensures that each feature contributes __________ to the distance computation.
- differently
- equally
- maximally
- minimally
Scaling features in KNN ensures that each feature contributes equally to the distance computation, preventing features with larger scales from dominating.
When multicollinearity is present in a dataset, it can make the coefficients of the variables ___________ and hard to interpret.
- insignificant
- reliable
- stable
- unstable
Multicollinearity can make the coefficients of the variables unstable and sensitive to small changes in the data. This makes the interpretation of individual coefficients unreliable and the model difficult to interpret.
A core point in DBSCAN is a point that has at least MinPts within _________ distance from itself.
- Epsilon
- border point
- cluster
- noise point
A core point in DBSCAN has at least MinPts within the Epsilon distance from itself. The Epsilon value defines the radius of the neighborhood around the point, and if there are enough points (MinPts or more) within this radius, the point is considered a core point.
You've developed a Polynomial Regression model with a high-degree polynomial, and it's performing exceptionally well on the training data but poorly on the test data. What might be the issue, and how would you address it?
- Add more features
- Increase the degree
- Reduce the degree or apply regularization
- Use a different algorithm entirely
The issue likely is overfitting due to the high-degree polynomial. Reducing the degree or applying regularization techniques like Ridge or Lasso can help to reduce the model's complexity and improve generalization to unseen data.
You are facing an overfitting problem in a linear model. How would you use Ridge, Lasso, or ElasticNet to address this issue?
- Decrease regularization strength
- Increase regularization strength
- Remove all regularization
- nan
Increasing the regularization strength can help to prevent overfitting by constraining the model complexity and reducing variance.
Can you list some applications of Machine Learning?
- Finance, Cooking
- Games, Cooking
- Games, Healthcare
- Healthcare, Finance, Marketing
Machine Learning is applied in various domains such as healthcare (for predicting diseases, personalizing treatments), finance (for fraud detection, risk management), marketing (for customer segmentation, targeted advertising), and more. Its versatility has made it an essential tool in modern technology.
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.