The Gini Index in a Decision Tree aims to minimize the probability of __________.
- Misclassification
- Optimization
- Overfitting
- Underfitting
The Gini Index in a Decision Tree aims to minimize the probability of misclassification. It quantifies how often a randomly chosen element from the set would be incorrectly labeled, guiding the best splits in the tree.
_________ learning is a type of Machine Learning where the model learns by interacting with an environment to achieve a goal.
- Reinforcement
- Semi-supervised
- Supervised
- Unsupervised
Reinforcement learning is a type of learning where an agent learns to make decisions by interacting with an environment, receiving feedback in the form of rewards or penalties.
When the assumptions of normality and homogeneity of variances are violated, LDA may provide ___________ results.
- biased
- consistent
- optimal
- suboptimal
If the assumptions of normality and homogeneity of variances are violated, LDA may provide "suboptimal" results, affecting its effectiveness in separating classes.
In K-Means clustering, the initial placement of centroids can be done using the _________ method, among others.
- K-Means++
- Mean Shift
- Random
- Silhouette
The K-Means++ method is commonly used for the initialization of centroids in K-Means clustering. It helps in faster convergence and reduces the risk of local minima by selecting initial centroids in a smarter way.
You have two very similar clusters in your dataset that DBSCAN is not separating well. What might be the problem and how could you resolve it?
- Increase Epsilon; Decrease MinPts
- Increase Epsilon; Increase MinPts
- Reduce Epsilon; Keep MinPts the same
- Reduce both Epsilon and MinPts
If DBSCAN is not separating two very similar clusters well, it may be due to the Epsilon being too large, causing the clusters to merge. Reducing Epsilon while keeping MinPts the same can make the algorithm more sensitive to slight differences, allowing it to differentiate between the similar clusters.
What is the difference between training and testing datasets in Machine Learning?
- Training for clustering; Testing for regression
- Training for labeling; Testing for predicting
- Training used to evaluate; Testing used to predict
- Training used to learn patterns; Testing used to evaluate performance
In Machine Learning, the training dataset is used for the model to learn patterns, and the testing dataset is used to evaluate the model's performance on unseen data.
What is the main difference between supervised and unsupervised learning?
- Application
- Complexity
- Data size
- Use of labeled data
The main difference is the use of labeled data. Supervised Learning uses labeled data, while Unsupervised Learning does not.
Explain the concept of k-fold Cross-Validation. What does "k" signify?
- Number of equally-sized folds the data is divided into
- Number of features in the dataset
- Number of iterations in training
- Number of layers in a deep learning model
In k-fold Cross-Validation, "k" signifies the number of equally-sized folds the data is divided into. The model is trained on (k-1) folds and validated on the remaining fold, repeating this process k times. The average performance across all k trials provides a more unbiased estimate of the model's capability.
What role does 'MinPts' play in the DBSCAN algorithm?
- Minimum Distance Between Points
- Minimum Percentage of Cluster Separation
- Minimum Points to Form a Cluster
- Minimum Potential for a Cluster
'MinPts' in DBSCAN refers to the minimum number of points required to form a dense region. It's used in conjunction with the Epsilon parameter to decide whether a particular region can be considered a cluster. It controls the density requirement for clustering, determining how many points must be within the Epsilon radius for a region to be considered dense.
Describe the role of hyperparameter tuning in the performance of a Machine Learning model.
- It adjusts the weights during training
- It optimizes the model's parameters before training
- It optimizes the values of hyperparameters to improve the model's performance
- It selects the type of model to be used
Hyperparameter tuning involves optimizing the values of hyperparameters (parameters set before training) to improve the model's performance. It helps in finding the best combination of hyperparameters that provides optimal performance for a given dataset.