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Questions and Answers

What is the primary goal of classification in machine learning?

Dividing data into distinct categories (correct)

Maximizing the number of features in a model

Predicting continuous values

Estimating missing values

Which machine learning model is commonly used for regression tasks?

Linear Regression (correct)

K-Means Clustering

Decision Trees

Logistic Regression

In which of the following scenarios would you use a regression model?

Classifying emails as spam or not spam

Estimating the time it takes to complete a marathon (correct)

Grouping similar customers together

Predicting whether a fruit is an apple or an orange

What type of target variable is predicted by classification algorithms?

Categorical labels Signup and view all the answers

Which of the following is an example of a regression problem?

Predicting the salary of an employee based on their experience Signup and view all the answers

Logistic Regression is typically used for which type of machine learning task?

Classification Signup and view all the answers

Which of the following is an example of a binary classification problem?

Predicting whether a student passes or fails an exam Signup and view all the answers

Which metric is commonly used to evaluate regression models?

Mean Squared Error (MSE) Signup and view all the answers

If the target variable is a continuous number, what type of problem is this?

Regression Signup and view all the answers

What is the purpose of using a confusion matrix?

A confusion matrix summarizes the performance of a machine learning model, specifically for classification tasks, by showing the number of correct and incorrect predictions. Signup and view all the answers

What does the term 'hyperparameter' refer to in machine learning?

Hyperparameters are the parameters that are set prior to the training process and are not learned from the data. They control the learning process and influence the model's performance. Signup and view all the answers

What is the difference between classification and regression in machine learning?

Classification deals with predicting categorical labels (discrete values) while regression focuses on predicting continuous values. Signup and view all the answers

Which type of machine learning is concerned with finding patterns in data without labeled examples?

Unsupervised learning Signup and view all the answers

What is the main difference between supervised and unsupervised learning?

Supervised learning uses labeled data, while unsupervised learning does not. Signup and view all the answers

Overfitting occurs when a model is too complex and performs well on the training data but poorly on unseen data.

True Signup and view all the answers

Underfitting occurs when a model is too simple and fails to capture the underlying patterns in the data, resulting in poor performance both on training and test data.

True Signup and view all the answers

What is the primary goal of a loss function in machine learning?

The primary goal of a loss function is to quantify how well or badly a machine learning model is performing by measuring the difference between the predicted and actual values. Signup and view all the answers

Cross-validation is a technique used to assess the model's performance on unseen data by splitting the data into multiple folds and using different folds for training and testing.

True Signup and view all the answers

Explain the concept of bias-variance tradeoff in machine learning.

Bias-variance tradeoff refers to the balance between a model's tendency to make systematic errors (bias) and its sensitivity to fluctuations in the training data (variance). Signup and view all the answers

What are some techniques to address the bias-variance tradeoff?

Some techniques to address the bias-variance tradeoff include: Good model selection, regularization (L1, L2), dimensionality reduction, and ensemble methods. Signup and view all the answers

K-means clustering is a supervised learning algorithm used for grouping similar data points.

False Signup and view all the answers

Study Notes

Machine Learning Lecture Notes

Lecture 12 Introduction to Machine Learning
- Instructor: Keerthana Vinod Kumar
- PMRF Scholar, Koita Centre for Digital health
- Indian Institute of Technology Bombay
Gamma Hyperparameter
- Higher gamma value: Decision boundary closer to points, more jagged, prone to overfitting
- Lower gamma value: Smoother, less prone to overfitting
- Applicable to all hyperplanes; Easier to observe in higher dimensions
- Sometimes referred to as sigma
Assignment 4
- Use diabetes dataset (onedrive link provided)
- Perform Exploratory Data Analysis (EDA): Overall info, dataset description, check for null values, data visualization, data scaling
- Separate features and target variable
- Perform train-test split (80:20)
- Perform classification using SVM, Random Forest, KNN, and Decision Tree
- Calculate evaluation metrics: Confusion matrix, Accuracy, Precision, F1-score, Recall, TPR, FPR, ROC-AUC
- Store results in a DataFrame
- Evaluate which algorithm performs best and give reasons
- Upload assignment

Model Evaluation Metrics

Confusion Matrix
- Summarizes model performance on test data
- Shows accurate/inaccurate instances
- Used to evaluate classification models
- Displays instances produced by the model
- TP (True Positive): Correctly predicts positive
- TN (True Negative): Correctly predicts negative
- FP (False Positive): Incorrectly predicts positive
- FN (False Negative): Incorrectly predicts negative
AUC-ROC
- ROC (Receiver Operating Characteristic) curve
- Shows classification model performance for different thresholds.
- Plotted between True Positive Rate (TPR) and False Positive Rate (FPR)
  - TPR = TP / (TP + FN)
  - FPR = FP / (FP + TN)
  - ROC curve analysis: Higher AUC implies better classification performance
- Perfect performance corresponds to (TPR=1, FPR=0)
Accuracy, Precision, Recall and F1
- Accuracy
- Proportion correct predictions to total predictions
- Precision
- Proportion of true positives among all positive predictions
- Precision = TP / (TP + FP)
- Recall (Sensitivity)
- Proportion of true positive predictions among all actual positives
- Recall = TP / (TP + FN)
- F1-Score
- Harmonic mean of precision and recall
- F1-score balances precision and recall

Hyperparameter Tuning

GridSearchCV
- Used for finding the best hyperparameter combination
Hyperparameters: Settings set before training; Non-learned from data.
- Example: Number of trees in random forest, kernel type, parameter in SVM
Kernel Function: Determines the similarity measure between data points
- Used in higher-dimensional spaces for classification
C (Hyperparameter): Affects margin size
- Larger C: Smaller margin, to avoid misclassifying training samples (regularization)
- Smaller C: Larger margin, allowing more misclassifications
Gamma (Hyperparameter): Also related to the distance of a point from decision boundary
- Higher gamma: Closer points considered for boundary, increasing potential for overfitting
- Lower gamma: Further points considered, smoother boundary, decreasing overfitting chance

Kernel Hyperparameters

Linear Kernel: Straight line to separate data points (works well for linearly separable data).
Polynomial Kernel: Creates curved or complex shapes for non-linear but not too complex relationships between data points.
Radial Basis Function Kernel (RBF): (Gaussian kernel) Determines similarity based on the distance between points; good for complex relationships in data.
Sigmoid Kernel: Based on the hyperbolic tangent function; less common in practice.

Classification Algorithms

Logistic Regression: Predicts categorical variables using probabilistic values (between 0 to 1) to represent the probability of either output (0 or 1).
Support Vector Machines (SVM): Creates the best line (hyperplane) that separates the data points into their respective categories in order to accurately group new data.
K-Nearest Neighbors (KNN): Assigns new data points to the class or category which the closest neighbors belong to based on a similarity function (e.g., Euclidean distance) and the closest 'K' neighbors to the new data points.
Decision Trees: Tree-structured classifier; internal nodes represent feature values, branches represent decision rules, and each leaf node represents the outcome
Random Forest: Building numerous decision trees using random subsets of the dataset to minimize the risk of overfitting while improving prediction performance.

Regression Algorithm

Linear Regression: Predicts continuous variables based on a relationship between the dependent variable and one or more independent variables.
Describes a linear relationship between variables.

Clustering

K-Means Clustering: Groups unlabeled data points into distinct clusters based on the similar characteristics of the data points.

Types of Datasets/Problems

Classification: Predicting a class or discrete value (e.g., male/female, spam/not spam)
Regression: Predicting a quantity or continuous value (e.g., salary, house price)

Exercises

Classification goal: Dividing data into distinct categories
Regression model: Linear Regression
Regression scenarios: Estimating time, predicting sales.
Binary Classification: Predicting pass/fail, spam/not spam.
Problem with continuous target variable: Regression.

Probability

Probability: Measure of the likelihood that an event will occur.
Ranges from 0 to 1.

Statistics

Central Tendency: Mean, Median, Mode
- Mean: Average of values
- Median: Middle value
- Mode: Most frequent value
Variability: Range, Variance, Standard Deviation
- Range: Difference between highest and lowest values
- Variance: Average of squared deviations from the mean
- Standard Deviation: Square root of the variance
Correlation: Relationship between two variables; does not imply causation.
Causation: Relationship between cause and effect.
Statistical Study Designs:
- Sample Study: Selecting a subset to study the entire population; calculate parameters from the sample.
- Observational Study: Observing without manipulating variables; assesses association, not causation.
- Experimental Study: Manipulating variables to assess causation.