Supervised Learning
This type of machine learning is used when the input data is labeled.
What is supervised learning?
This type of machine learning is used when the input data is unlabeled.
What is unsupervised learning?
This type of error occurs when a model is too simple and cannot capture the complexity of the data.
What is underfitting?
This metric measures the proportion of true positives among all positive predictions.
What is precision?
This optimization algorithm uses the gradient of the loss function to update the model parameters.
What is gradient descent?
This algorithm is used to classify data into two distinct categories.
What is a binary classifier?
This algorithm is used to group similar data points together.
What is clustering?
This type of error occurs when a model is too complex and captures noise in the data.
What is overfitting?
This metric measures the proportion of true positives among all actual positive instances.
What is recall?
This optimization algorithm is used to find the global minimum of a function.
What is the Genetic algorithm?
This technique is used to prevent overfitting in machine learning models.
What is regularization?
This technique is used to reduce the dimensionality of high-dimensional data.
What is dimensionality reduction?
This is the trade-off between bias and variance in machine learning models.
What is the bias-variance tradeoff?
This metric combines precision and recall into a single score.
What is the F1-score?
This optimization algorithm is used to find the optimal parameters for a model with multiple local minima.
What is simulated annealing?
This is the process of adjusting model parameters to minimize the difference between predicted and actual values.
What is model training?
This is the process of finding patterns and relationships in data without any specific goal in mind.
What is exploratory data analysis?
This is the type of error that occurs when there is a systematic difference between the predicted and actual values.
What is bias?
This metric measures the area under the Receiver Operating Characteristic (ROC) curve.
What is the Area Under the Curve (AUC)?
This optimization algorithm is used to find the optimal parameters for a model with a large number of features.
What is the Particle Swarm Optimization (PSO) algorithm?
This is the measure of how well a model performs on unseen data.
What is generalization?
This is the measure of how well a model can separate different groups of data.
What is cluster separation?
This is the type of error that occurs due to the sensitivity of the model to the training data.
What is variance?
This metric measures the average squared difference between the predicted and actual values.
What is mean squared error (MSE)?
This optimization algorithm is used to find the optimal parameters for a model with a large number of constraints.
What is the Constrained Optimization by Linear Approximation (COBYLA) algorithm?