Machine Learning Flashcards

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Bias-Variance Tradeoff
The bias-variance tradeoff represents the balance between a model's simplicity (bias) and its sensitivity to fluctuations in the training data…
Mean Shift Clustering
Mean Shift clustering is an unsupervised algorithm used to discover clusters in data without the need for specifying the number of clusters in advance…
Stochastic Gradient Descent With Momentum
Stochastic gradient descent with momentum uses an exponentially weighted average of past gradients to update the momentum term and the model's…
July 2023
RMSprop
RMSprop optimizer adjusts the learning rates of each parameter based on the root mean square of the past gradients. It uses a moving average of the…
Out-Of-Bag Errors
Out-of-bag (OOB) error is a measure used in ensemble learning, specifically in random forest algorithms.
Adaboost
AdaBoost, or Adaptive Boosting, is an ensemble learning technique that combines multiple weak classifiers to form a strong classifier.
Common Output Layer Activation Functions
An output layer activation function processes the outputs of the neural network's final layer to produce a desired range of outputs, such as a…
June 2023
Epoch
An epoch is a single iteration of training a neural network on the entire training set.
Loss Functions
A loss function, also called a cost function, measures the difference between the predicted output of a machine learning model and the true output…
Effect Of Feature Scaling On Gradient Descent
Feature scaling, such as normalization or standardization, ensures that all features are on a similar scale, allowing gradient descent to avoid…
DBSCAN
DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is a clustering algorithm used to group samples that are close to each other in the…
May 2023
Random Forests
Random Forests is an ensemble learning method that combines multiple decision trees to make predictions.
© 2023 Chris Albon
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