Machine Learning Fundamentals Flashcards
7 cards from real Data and Analytics practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Machine Learning Fundamentals flashcards as text
Which algorithm builds an ensemble of decision trees using random subsets of features and training samples?
Answer: Random Forest
Random Forest creates multiple decision trees using random feature subsets and bootstrap samples, then aggregates their predictions to improve accuracy and reduce overfitting.
In a neural network, what is the role of an activation function?
Answer: To introduce non-linearity so the network can learn complex patterns
Activation functions introduce non-linearity into neural networks, enabling them to learn and represent complex, non-linear relationships in data.
What is gradient descent used for in machine learning?
Answer: Iteratively updating model parameters to minimize the loss function
Gradient descent is an optimization algorithm that iteratively adjusts model parameters in the direction that minimizes the loss function, guided by the gradient.
Which type of machine learning is used in recommendation systems where an agent learns by receiving rewards or penalties?
Answer: Reinforcement learning
Reinforcement learning trains an agent to make decisions by rewarding desired actions and penalizing undesired ones, making it well-suited for sequential decision-making tasks.
What does PCA (Principal Component Analysis) primarily accomplish in data analysis?
Answer: Reduces dimensionality by projecting data onto principal components
PCA reduces the number of features by projecting data onto orthogonal axes (principal components) that capture the most variance, simplifying analysis without losing critical information.
In logistic regression, what does the output of the sigmoid function represent?
Answer: The probability that a data point belongs to a particular class
The sigmoid function maps any real value to a probability between 0 and 1, representing the likelihood that an input belongs to the positive class.
What is transfer learning in the context of deep learning?
Answer: Reusing a pre-trained model on a new but related task
Transfer learning leverages knowledge from a model trained on a large dataset and fine-tunes it for a new, related task, reducing training time and data requirements.