Machine Learning Accuracy, Precision, and Loss Functions Flashcards

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Computer Science - Artificial Intelligence

user_striner Created by 7 mon ago

Cards in this deck(31)

quantifies the difference between a model's predictions and the observed values.

indicate the model performs better at predicting the outcome.

To fit models that classify instances relative to a decision boundary.

is a table that summarizes the combinations of predicted and actual values. For binary classifiers this is a table with two rows and two columns and gives the number of true positives, true negatives, false positives, and false negatives.

is an outcome that is correctly predicted as positive.

is an outcome that is predicted as negative but is actually positive.

is an outcome that is predicted as positive but is actually negative.

is an outcome that is correctly predicted as negative.

of a classifier is the proportion of correct positive predictions and calculated as:

of a classifier is the proportion of correctly predicted positive instances and calculated as:

of a classifier is the proportion of correct predictions and is calculated as:

models or predicts a specific quantile of the numeric output feature based on the input features.

is used for fitting quantile regression models

is the square root of the average of the squared differences between observed and predicted values:

is a plot showing how well a classification model distinguishes between binary classes at various thresholds by plotting the true positive rate (recall) vs. the false positive rate.

is the process of estimating parameters for a machine learning model.

is the process of evaluating a model's initial performance and adjusting parameter estimates or hyperparameter settings if needed.

is the process of evaluating a final model's performance. Ideally, an unseen set of data would be available to test the trained model.

uses different subsets of the data for model training and model testing.

is used to fit the initial model.

is used to decide optimal hyperparameter values or assess whether a model is overfitted or underfitted.

is used to evaluate a model's performance or select between competing models.

is the process of selecting the best hyperparameter for a model using cross-validation.

plot the mean cross-validation scores for each hyperparameter value tested in model tuning.

uses the k most similar instances to a data point to impute the missing values.

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Machine Learning Accuracy, Precision, and Loss Functions Flashcards

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