Machine Learning Quiz & Flashcards

Supervised learning uses labeled data to train models, unlike unsupervised learning which groups data by similarity.

K-means is a popular clustering algorithm, while logistic regression and SVMs are used for classification.

High bias indicates the model is too simple and may not capture the underlying trend of the data.

Regularization adds a penalty for complexity to the loss function to prevent overfitting.

Random Forest is an ensemble method using multiple decision trees, while K-means is for clustering.

PCA stands for Principal Component Analysis, a method for dimensionality reduction.

ReLU is commonly used in hidden layers, while softmax is often used in output layers of classification networks.

RNNs are well-suited for time series analysis due to their ability to process sequences of data.

A confusion matrix evaluates classification model performance by showing actual vs. predicted classifications.

A kernel function transforms data into a higher-dimensional space to enable linear separation in SVM.

Clustering is an unsupervised learning task, whereas classification and regression are supervised tasks.

Decision trees can easily overfit, especially with complex datasets, though they are easy to interpret.

PCA is used for dimensionality reduction, while SVM is used for classification.

A validation set is used to tune hyperparameters and avoid overfitting during model training.

Data augmentation increases dataset size by creating variations of existing data to improve model robustness.

Neural networks are inspired by the human brain's structure, using layers of neurons to process data.

Reinforcement learning aims to maximize cumulative reward through interaction with an environment.

A 'black box' model has non-transparent decision-making processes, making it difficult to interpret.

Dropout is a regularization method used to prevent overfitting by randomly dropping units during training.

Scalability refers to a model's ability to efficiently handle increasing amounts of data.

Ensemble methods often achieve improved predictive performance by combining multiple models.

CNNs are commonly used in image processing due to their ability to capture spatial hierarchies.

Decision Tree is not a neural network type; it's a separate model used for decision-making tasks.

The F1 score measures the balance between precision and recall, providing a single metric for performance.

Early stopping helps prevent overfitting by stopping training when model performance on a validation set starts to degrade.

The bias-variance tradeoff addresses the need to balance model simplicity (bias) and complexity (variance) for optimal performance.

Stochastic gradient descent updates model weights for each training example, unlike batch gradient descent.

Transfer learning is beneficial when labeled data is limited, allowing models to leverage pre-trained knowledge.

A loss function measures prediction error, guiding the optimization of model parameters.

A common misconception is that machine learning models can solve any problem, but they require appropriate data and context.

Neural networks excel at learning complex patterns in data, unlike many traditional algorithms.

A 'white box' model has transparent decision-making processes, making it easier to interpret.

Batch normalization stabilizes the learning process by normalizing inputs to each layer, improving convergence speed.

Generative models create new data instances, often used in tasks like image generation or unsupervised learning.

Autoencoders are neural networks used primarily for dimensionality reduction and feature learning.

The softmax layer converts the network's output scores into probabilities, facilitating multi-class classification.

K-means is an unsupervised learning algorithm, while the others are used for supervised learning tasks.

Cross-validation assesses how well a model generalizes to an independent dataset, preventing overfitting.

RNNs are designed to process sequential data, making them suitable for tasks like language modeling and time series analysis.

t-SNE is a technique specifically designed for visualizing high-dimensional datasets by reducing them to two or three dimensions.

Feature engineering involves selecting and transforming variables to improve model performance on a given task.

Dropout layers help prevent overfitting by randomly setting a portion of neurons to zero during training.

Unsupervised learning is used to find patterns in unlabeled data, unlike supervised learning tasks like classification.

Decision boundary plotting specifically visualizes the separator between different classes in a classifier.

Model interpretability is the ability to understand and explain the decisions made by a machine learning model.

Gradient descent is an optimization algorithm used to minimize the loss function by adjusting model parameters.

Decision trees are prone to high variance and overfitting, especially with complex datasets, unlike simpler models like linear regression.

Deep learning models often have high computational costs due to their complexity and large number of parameters.

A validation set is used during model training for hyperparameter tuning and to prevent overfitting.

A confusion matrix is used to assess the accuracy of a classification model by comparing predicted and actual classes.