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Questions and Answers
What effect does the choice of kernel function have in kernel density estimation?
What effect does the choice of kernel function have in kernel density estimation?
Which kernel function is commonly used in kernel density estimation?
Which kernel function is commonly used in kernel density estimation?
In k-nearest neighbor density estimation, what does the parameter 'k' represent?
In k-nearest neighbor density estimation, what does the parameter 'k' represent?
What is a key advantage of k-nearest neighbor density estimation?
What is a key advantage of k-nearest neighbor density estimation?
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Which nonparametric method is best suited for handling large datasets with unknown distribution shapes?
Which nonparametric method is best suited for handling large datasets with unknown distribution shapes?
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What is the primary difference between histogram estimators and kernel estimators?
What is the primary difference between histogram estimators and kernel estimators?
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What limitation is commonly associated with kernel density estimation techniques?
What limitation is commonly associated with kernel density estimation techniques?
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What does a histogram estimator primarily rely on for its structure?
What does a histogram estimator primarily rely on for its structure?
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Which clustering algorithm can handle clusters of varying shapes and sizes?
Which clustering algorithm can handle clusters of varying shapes and sizes?
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Which clustering algorithm does not require the assumption of equal-sized clusters?
Which clustering algorithm does not require the assumption of equal-sized clusters?
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Which clustering algorithm is based on the concept of nearest neighbors?
Which clustering algorithm is based on the concept of nearest neighbors?
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Which assumption does the Naïve Bayes classifier make about features?
Which assumption does the Naïve Bayes classifier make about features?
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Which probability is calculated in the Naïve Bayes algorithm to classify a new data point?
Which probability is calculated in the Naïve Bayes algorithm to classify a new data point?
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What is the key equation used in Bayes' Theorem?
What is the key equation used in Bayes' Theorem?
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In a Naïve Bayes classifier, which class is chosen as the predicted class?
In a Naïve Bayes classifier, which class is chosen as the predicted class?
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What is the main purpose of the 'kernel trick' in SVM?
What is the main purpose of the 'kernel trick' in SVM?
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Which kernel function is commonly used in Support Vector Machines (SVM) for non-linearly separable data?
Which kernel function is commonly used in Support Vector Machines (SVM) for non-linearly separable data?
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Which of the following is NOT a commonly used kernel in SVM?
Which of the following is NOT a commonly used kernel in SVM?
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In Bayes' Theorem, what does the term $P(B)$ represent?
In Bayes' Theorem, what does the term $P(B)$ represent?
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Which statement about Naïve Bayes classifiers is accurate?
Which statement about Naïve Bayes classifiers is accurate?
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What is a 'support vector' in the context of SVM?
What is a 'support vector' in the context of SVM?
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Which activation function is most commonly used in the output layer of a binary classification neural network?
Which activation function is most commonly used in the output layer of a binary classification neural network?
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What is the primary role of an activation function in a neural network?
What is the primary role of an activation function in a neural network?
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What is a Perceptron in the context of machine learning?
What is a Perceptron in the context of machine learning?
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What does the Simple Matching Coefficient measure?
What does the Simple Matching Coefficient measure?
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Which metric is used to calculate the correlation between two attributes?
Which metric is used to calculate the correlation between two attributes?
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What does the Cosine Similarity measure?
What does the Cosine Similarity measure?
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Which of the following measures similarity between binary vectors?
Which of the following measures similarity between binary vectors?
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What is a key advantage of using Euclidean Distance?
What is a key advantage of using Euclidean Distance?
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In what type of data is the Cosine Similarity particularly useful?
In what type of data is the Cosine Similarity particularly useful?
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What does a decision tree model do?
What does a decision tree model do?
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Which algorithm is commonly used to create a decision tree?
Which algorithm is commonly used to create a decision tree?
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What is a primary difference between histograms and kernel density estimators?
What is a primary difference between histograms and kernel density estimators?
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How does the choice of bandwidth affect kernel density estimation?
How does the choice of bandwidth affect kernel density estimation?
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Which statement accurately describes nonparametric methods?
Which statement accurately describes nonparametric methods?
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In nonparametric density estimation, what does 'smoothing' signify?
In nonparametric density estimation, what does 'smoothing' signify?
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What is commonly observed when increasing the number of bins in a histogram?
What is commonly observed when increasing the number of bins in a histogram?
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Which metric is generally the most useful when handling an imbalanced dataset?
Which metric is generally the most useful when handling an imbalanced dataset?
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What effect does kernel smoothing have compared to histograms in density estimation?
What effect does kernel smoothing have compared to histograms in density estimation?
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When using histograms, what happens if the bin size is too large?
When using histograms, what happens if the bin size is too large?
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How does the k-Means algorithm initialize cluster centroids?
How does the k-Means algorithm initialize cluster centroids?
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What is the role of the ‘k’ parameter in the k-Means algorithm?
What is the role of the ‘k’ parameter in the k-Means algorithm?
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How does the k-Means algorithm update cluster centroids during each iteration?
How does the k-Means algorithm update cluster centroids during each iteration?
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What is a major limitation of the k-Means algorithm?
What is a major limitation of the k-Means algorithm?
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How does the k-Means algorithm determine convergence?
How does the k-Means algorithm determine convergence?
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Which distance metric is commonly used in the k-Means algorithm?
Which distance metric is commonly used in the k-Means algorithm?
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What is the computational complexity of the k-Means algorithm?
What is the computational complexity of the k-Means algorithm?
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Which of the following methods can help improve the performance of the k-Means algorithm?
Which of the following methods can help improve the performance of the k-Means algorithm?
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Study Notes
Machine Learning Foundations - Unit 1
- Machine learning is primarily focused on building algorithms that allow computers to learn and improve from data.
- Examples of machine learning applications include predicting stock prices.
- Data in machine learning is the information used to train and test models.
- Supervised learning uses labeled data for learning.
- Unsupervised learning aims to discover patterns or structures in unlabeled data.
- Reinforcement Learning is a type of learning where an agent learns by interacting with an environment to maximize cumulative reward.
- Data accuracy, computational speed and model complexity are challenges in machine learning.
- The purpose of training in machine learning is to build a model that can predict or classify data.
- Generalization addresses the model's ability to perform well on unseen data.
Machine Learning Foundations - Unit 1 Additional Topics (cont'd)
- Feasibility of learning: The ability to learn effectively from available data.
- Model complexity: The intricacy of the model; crucial to prevent overfitting.
- Cross-validation: A measure of the discrepancy between training and testing performance.
- Underfitting: A model that is too simplistic and fails to capture the underlying patterns in the data.
- Overfitting: A model that is too complex and fits the noise in the training data but doesn't generalize to new data.
- Bias-variance tradeoff: Balance between the model fitting the training data perfectly and not over-fitting.
- Distance Metrics: Various measures for quantifying how far apart data points are, including Euclidean distance, Manhattan distance, and Minkowski distance.
- Cosine similarity: A measure, useful for text data, of the angle between two vectors.
- Jaccard coefficient: Measures the similarity between sets, common in text data.
- Simple Matching Coefficient: Measures the proportion of matching attributes in binary data.
- Pearson Correlation Coefficient: Used to calculate the correlation between two variables.
- Distance Metrics: Euclidean distance, Manhattan distance, Minkowski distance, Cosine Similarity, Jaccard coefficient.
- K-Nearest Neighbors (KNN): An algorithm where a new data point is classified based on the categories of the most similar nearby points.
- KNN Challenges:: Complexity of decision boundary and high computational cost during prediction.
- Decision Trees: Algorithm that creates a tree-like structure, effectively partitioning data based on feature values leading to predictions.
- Decision Tree Algorithms: Apriori, ID3, and C4.5 are widely known.
- Rule-Based Classifiers': Employ pre-defined if-then rules to classify data.
- Polynomial Regression: Uses polynomial terms to capture non-linear relationships between variables.
- Multicollinearity: Occurs when independent variables in a linear regression model are highly correlated.
- Regularization Techniques: Method employed in models to prevent overfitting and improve generalization to new data like Ridge Regression and Lasso Regression.
- Model Evaluation Metrics for Regression : MSE (Mean Squared Error), MAE (Mean Absolute Error), RMSE (Root Mean Squared Error), RMSLE (Root Mean Squared Logarithmic Error)
Clustering Analysis - Unit 2
- k-Means Clustering: Aims to partition data into 'k' clusters based on minimizing the within-cluster variance.
- Hierarchical Clustering: Creates clusters in a hierarchical structure (either agglomerative or divisive).
- Agglomerative Clustering: Starts with each data point as a separate cluster and merges them based on the minimum distance.
- Divisive Clustering: Starts with all data points in one cluster and recursively splits them into smaller clusters.
- Ward's Method: A Hierarchical clustering method that minimizes the sum of squared differences within clusters.
- Dendrogram: A tree-like diagram showing the hierarchical relationship between clusters.
- Silhouette Coefficient: A measure of how similar a data point is to its own cluster compared to other clusters.
- DBSCAN (Density-Based Spatial Clustering of Applications with Noise): Clusters data points based on density, identifying outliers as noise.
- Core Points: Data points surrounded by a minimum number of points within a specific radius.
- K-nearest neighbors (KNN): used in algorithms like DBSCAN
- Feature scaling/data normalization: Improves the performance of some algorithms like KMeans.
Naïve Bayes and Support Vector Machines (SVM) - Unit 3
- Naive Bayes: A probabilistic classifier based on Bayes' Theorem assuming features are conditionally independent given the class label.
- Support Vector Machines (SVM): A supervised learning method that finds the optimal hyperplane to separate data points of different classes.
- Kernel Trick: Transforms the data into a higher-dimensional space to solve non-linearly separable problems.
- Linear Kernel: Used for linearly separable data.
- Polynomial Kernel: Used for non-linearly separable data.
- Gaussian Kernel: Also known as RBF (Radial Basis Function) kernel. Used for non-linearly separable data.
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