If the assumptions of Simple Linear Regression are violated, the coefficient estimates may become _________, and predictions may not be reliable.
- Biased
- Efficient
- Improved
- Optimized
If the assumptions of Simple Linear Regression are violated, the coefficient estimates may become biased, leading to unreliable predictions.
What is the F1-Score, and why might you use it instead of Precision and Recall?
- Arithmetic mean of Precision and Recall
- Geometric mean of Precision and Recall
- Harmonic mean of Precision and Recall
- nan
The F1-Score is the harmonic mean of Precision and Recall. It balances both metrics and is particularly useful when you need to seek a balance between Precision and Recall and there is an uneven class distribution.
In Simple Linear Regression, the method of _________ is often used to estimate the coefficients.
- Clustering
- Gradient Descent
- Least Squares
- Neural Networks
The method of least squares is commonly used in Simple Linear Regression to estimate the coefficients by minimizing the sum of squared errors.
Dimensionality reduction can help in mitigating the problem of ___________, which refers to the difficulties of analyzing data in a high-dimensional space.
- multicollinearity
- overfitting
- scaling problems
- the curse of dimensionality
The term "curse of dimensionality" refers to the difficulties that arise when analyzing and organizing data in high-dimensional spaces. Dimensionality reduction can mitigate this problem by reducing the number of dimensions, making the data more manageable.
How does Cross-Validation help in reducing overfitting?
- By adding noise to the data
- By allowing a more robust estimate of model performance
- By increasing the dataset size
- By regularizing the loss function
Cross-Validation reduces overfitting by allowing for a more robust estimate of the model's performance. By using different splits of the data, it ensures that the model's validation is not overly reliant on a specific subset, helping to detect if the model is overfitting to the training data.
The Logit function can be defined as the logarithm of the __________ of the probability of the event occurring.
- Difference
- Odds
- Product
- Sum
The Logit function is defined as the logarithm of the odds of the probability of the event occurring.
What is the mathematical criterion that K-Means attempts to minimize, and how does it relate to centroid initialization?
- Maximizing centroid distances to data points
- Maximizing inter-cluster distance
- Minimizing the number of clusters
- Minimizing the sum of squared distances to centroids
K-Means minimizes the sum of squared distances from each point to its assigned centroid. Centroid initialization affects how quickly this criterion is minimized and the quality of the final clusters.
If two attributes in a Decision Tree have the same entropy, the attribute with the __________ Gini Index would generally be preferred.
- Equal
- Higher
- Lower
- Random
If two attributes in a Decision Tree have the same entropy, the attribute with the lower Gini Index would generally be preferred. A lower Gini Index indicates a purer node and would typically result in a better split.
Which type of Machine Learning algorithm would be best suited for predicting a continuous value?
- Classification
- Clustering
- Regression
- Reinforcement Learning
Regression algorithms are designed to predict continuous values, such as stock prices or temperatures, by learning the relationship between independent and dependent variables.
You're designing a system for image recognition with a need for real-time response. Which approach would be more appropriate: Machine Learning or Deep Learning, and why?
- Both are equally appropriate
- Deep Learning, for its advanced image recognition capabilities
- Machine Learning, for its simpler models
- nan
Deep Learning, particularly Convolutional Neural Networks (CNNs), is highly effective for image recognition and is usually preferred for such tasks.