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Asymmetric-key algorithms are algorithms used in cryptography that use two different keys  a public key for encryption and a private key for decryption. These keys are mathematically related, but the private key cannot be easily derived from the public key. Types: RSA (Rivest–Shamir–Adleman): It uses large prime numbers to generate the key pair and supports both encryption and digital signatures DSA (Digital Signature Algorithm): DSA is primarily used for creating digital signatures, ensuring the authenticity. Symmetric-key algorithms are algorithms for cryptography that use the same cryptographic keys for both encryption of plaintext and decryption of ciphertext  Types: Stream Cipher:  Stream Cipher Converts the plain text into cipher text by taking 1 byte of plain text at a time. Block cipher: Converts the plain text into cipher text by taking plain text's block at a time DES? DES stands for Data Encryption Standard . It is a symmetric-key algorithm used to enc...

1. What kind of output does logistic regression predict? Logistic regression predicts the probability that a given input belongs to a certain class. For binary classification, this output is a number between 0 and 1. The predicted probability can then be converted into a class label (0 or 1) using a threshold, typically 0.5. 2. Is logistic regression used for classification or regression tasks? Logistic regression is primarily used for classification tasks, despite its name suggesting a regression method. It is especially used when the dependent variable is categorical, most commonly binary (e.g., spam vs. not spam, disease vs. no disease). 3. What shape is the curve of the logistic function? The logistic function produces an S-shaped curve, also called a sigmoid curve. This curve takes any real-valued number and maps it to a value between 0 and 1. It's especially useful for representing probabilities because it gradually flattens out as it approaches 0 or 1. 4. What value ...

SHORT QUESTIONS FOR HIERARCHICAL CLUSTERING General Introduction Hierarchical clustering is a way for computers to group similar things together without being told how. It builds a tree of clusters that shows how the groups are formed. This can be shown with a dendrogram (a tree-like diagram). There are two main types: Agglomerative (Bottom-Up): Start with each item in its own group, then slowly combine the closest ones. Divisive (Top-Down): Start with everything in one big group, then slowly split it into smaller ones. Q1. When should you use hierarchical clustering, and when not? Use hierarchical clustering when: ·  You want to understand groupings  or structure in data. ·  You're working with small to medium-sized datasets . ·  You need to visualize clustering with a dendrogram . Avoid it when: ·  You're dealing with very large datasets  (due to performance issues). ·  You need real-time or very fast clustering. ·  You have high nois...

SHORT QUESTIONS FOR CLUSTERING(K-MEANS) Q1. What is clustering in data mining? ANS.  Clustering is a method used in data mining to group similar data points together. The idea is that data points in the same group, called a cluster, are more alike than those in other groups.  It's an unsupervised learning technique, which means it doesn’t use labeled data or known answers. The main goal of clustering is to discover patterns or hidden structures in the data without any prior knowledge. Q2. Explain the K-means clustering algorithm. ANS.   Here’s a simpler version of your explanation: K-means is a popular clustering method that splits data into K groups (or clusters). Here’s how it works: Pick K starting points (called centroids), usually chosen randomly. Assign each data point to the nearest centroid , creating K clusters. Calculate new centroids by finding the average position of all points in each cluster. Repeat steps 2 and 3 until the centroids stop cha...

  SHORT QUESTIONS ON DECISION TREE 1. What is the main purpose of a decision tree in machine learning? A decision tree is a tool that helps make decisions by showing possible outcomes step-by-step. It works for both classification (sorting things into categories) and regression (predicting numbers). The tree splits the data into smaller groups based on tests on different features. Each internal node is a question about a feature, and each leaf node is the final decision or prediction. 2. What criterion is commonly used to split nodes in a decision tree? The most common ways to decide where to split a node in a decision tree are Gini impurity and Information Gain (which uses entropy). These measures show how well a split separates the data into different classes, with the goal of making each group as pure (or similar) as possible. 3. What is a leaf node in a decision tree? A leaf node , also called a terminal node , is the final point at the end of a path in a decision tree. ...

General Concepts & Problems Q: What is High-Performance Computing (HPC)? A: HPC is using parallel processing to run advanced applications that are either too large or would take too long for standard computers. It's a network of nodes, each with processing units and its own memory. As computer performance has increased, power consumption has also significantly increased. This leads to several problems. Q: What are the three main categories of problems? Economical Impact: High electricity bills (e.g., US desktop computers >10% of electricity). Large data centers (e.g., Google) consume as much electricity as a small town. Cooling can account for up to 70% of energy costs. Supercomputers might need entirely new buildings. Ecological Impact: Significant carbon dioxide (CO2) production (desktops like millions of cars). Worsening heat dissipation problems. IT industry's greenhouse gas emissions comparable to international airlines. Technical Impact: Large multiprocessors need ...