Machine Learning Question Bank Quiz and Flashcards

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Questions and Answers

Explain how the variance impacts Ridge and Lasso regression.

The Ridge regression adds penalty equivalent to the square of the magnitude of coefficients, while Lasso regression adds penalty equivalent to the absolute value of the coefficients. This helps in reducing the variance and dealing with overfitting in the models.

Discuss the Naïve Bayes Classifier with an example.

Naïve Bayes Classifier is a probabilistic classifier based on Bayes' theorem with the assumption of independence among predictors. For example, in text classification, it can be used to classify emails as spam or not spam based on the occurrence of certain words.

Explain the concept of R2 score.

R2 score, also known as the coefficient of determination, is a statistical measure that represents the proportion of the variance for the dependent variable that's explained by independent variables in a regression model. It ranges from 0 to 1, where 1 indicates a perfect fit.

Discuss the different types of multiclass strategies in data formats of machine learning.

Some common multiclass strategies include One-vs-All (OvA), One-vs-One (OvO), and Hierarchical classification. OvA treats each class as a binary classification problem, OvO compares each pair of classes, and Hierarchical classification divides the classes into a hierarchy. Signup and view all the answers

Explain how the ROC curve is constructed.

ROC curve is constructed by plotting the True Positive Rate (Sensitivity) against the False Positive Rate (1-Specificity) at various threshold settings. It gives a graphical representation of the trade-off between sensitivity and specificity. Signup and view all the answers

What are the main differences between supervised and unsupervised learnings in machine learning?

Supervised learning involves training on labeled data to make predictions on new data, whereas unsupervised learning involves training on unlabeled data to discover patterns or relationships. Signup and view all the answers

What is overfitting and underfitting in machine learning, and how do they differ?

Overfitting occurs when a model is too complex and performs well on training data but poorly on new data, whereas underfitting occurs when a model is too simple and performs poorly on both training and new data. Signup and view all the answers

What is the concept of Bayes' theorem, and how is it used in machine learning?

Bayes' theorem is a mathematical formula for updating the probability of a hypothesis based on new data, and is used in machine learning for probabilistic modeling and inference. Signup and view all the answers

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

Classification involves predicting a categorical label, whereas regression involves predicting a continuous value. Signup and view all the answers

What is stochastic gradient descent, and why is it commonly used in machine learning?

Stochastic gradient descent is an optimization algorithm that updates model parameters based on a single data point at a time, and is commonly used due to its efficiency and scalability. Signup and view all the answers

What is the purpose of cross-validation in machine learning, and how is it used?

Cross-validation is a technique for evaluating machine learning models by dividing the data into training and testing sets, and is used to prevent overfitting and evaluate model performance. Signup and view all the answers

Study Notes

Machine Learning Systems

Types of Machine Learning Systems:
- Classic Machine Learning Model: handles fixed data and provides a fixed output
- Adaptive Machine Learning Model: learns from new data and adapts to changes

Supervised and Unsupervised Learning

Supervised Learning: learns from labeled data to predict output
Unsupervised Learning: learns from unlabeled data to find patterns or relationships

Reinforcement Learning

Reinforcement Learning: learns from interactions with the environment to maximize a reward

Challenges of Machine Learning

Main Challenges:
- Overfitting
- Underfitting
- Hyperparameter tuning
- Model selection
- Data quality

Hyperparameters

Hyperparameters: parameters set before training a model, e.g. learning rate, batch size

Bayes' Theorem

Bayes' Theorem: probability theory that updates the probability of a hypothesis based on new data
Example: given a disease and a test, calculate the probability of having the disease given a positive test result

Overfitting and Underfitting

Overfitting: when a model performs well on training data but poorly on new data
Underfitting: when a model performs poorly on both training and new data
Solutions to Overfitting:
- Regularization
- Early stopping
- Data augmentation

Error Measures and Cost Functions

Error Measures: metrics used to evaluate model performance, e.g. mean squared error, accuracy
Cost Functions: objective functions to minimize, e.g. mean squared error, cross-entropy

Classification and Regression

Classification: predicts a categorical output
Regression: predicts a continuous output
Binary Classification: predicts one of two classes
Multiclass Classification: predicts one of multiple classes

Predictive Analysis

Types of Predictive Analysis:
- Descriptive analysis: summarizes data
- Predictive analysis: predicts future outcomes
- Prescriptive analysis: recommends actions

Logistic Regression

Logistic Regression: predicts a probability of a binary output
Applications: credit risk assessment, customer churn prediction

Model Evaluation Metrics

Confusion Matrix: summarizes prediction results
ROC Curve: plots true positive rate vs. false positive rate
Performance Metrics: accuracy, precision, recall, sensitivity, specificity

Bayes' Theorem Example

Example: given a disease and a test, calculate the probability of having the disease given a positive test result

Stochastic Gradient Descent

Stochastic Gradient Descent: optimization algorithm for large datasets
Need: handles large datasets, reduces computation time

Regularization in Decision Trees

Regularization Hyperparameters: parameters to control model complexity, e.g. pruning, maximum depth

Cross-Validation

Cross-Validation: evaluates model performance by splitting data into training and testing sets
Example: 5-fold cross-validation splits data into 5 folds, trains and tests on each fold

Naïve Bayes Classifier

Naïve Bayes Classifier: simple probabilistic classifier, assumes independence of features
Example: given a text and a set of keywords, classify as spam or not spam

R2 Score

R2 Score: measures the goodness of fit of a regression model

Multiclass Strategies

Types of Multiclass Strategies:
- One-vs-All (OVA)
- One-vs-One (OVO)
- Hierarchical classification

ROC Curve

ROC Curve: plots true positive rate vs. false positive rate
Construction: plots the ROC curve using true positive rate and false positive rate

Huber Regression

Huber Regression: robust regression method, reduces the impact of outliers

Polynomial Regression

Polynomial Regression: predicts a continuous output using a polynomial equation
Variance: affects the model's performance, e.g. overfitting or underfitting

Principal Component Analysis

Principal Component Analysis: reduces dimensionality by retaining the most important features
Applications: image compression, facial recognition

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Description

This quiz covers different types of machine learning systems, compares classic and adaptive machine learning models, discusses major applications, compares supervised and unsupervised learning, explains reinforcement learning, challenges in machine learning, hyperparameters, Bayes' theorem, and overfitting.

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