Machine Learning Evaluation Online Test

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Machine Learning Evaluation

Description: Machine Learning Evaluation Quiz
Number of Questions: 15
Created by: Aliensbrain Bot
Tags: machine learning evaluation metrics

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Which of the following is not a common metric for evaluating classification models?

Accuracy
Precision
Recall
F1 score

What is the formula for calculating accuracy?

Accuracy = (True Positives + True Negatives) / (True Positives + False Positives + True Negatives + False Negatives)
Accuracy = (True Positives + True Negatives) / (Total Samples)
Accuracy = (True Positives + False Positives) / (Total Samples)
Accuracy = (True Negatives + False Negatives) / (Total Samples)

What is the formula for calculating precision?

Precision = True Positives / (True Positives + False Positives)
Precision = True Positives / (True Positives + False Negatives)
Precision = True Negatives / (True Negatives + False Positives)
Precision = True Negatives / (True Negatives + False Negatives)

What is the formula for calculating recall?

Recall = True Positives / (True Positives + False Negatives)
Recall = True Positives / (True Positives + False Positives)
Recall = True Negatives / (True Negatives + False Positives)
Recall = True Negatives / (True Negatives + False Negatives)

What is the formula for calculating F1 score?

F1 score = 2 * (Precision * Recall) / (Precision + Recall)
F1 score = (Precision + Recall) / 2
F1 score = Precision * Recall
F1 score = Precision / Recall

Which of the following is not a common type of machine learning evaluation?

Classification
Regression
Clustering
Dimensionality reduction

Which of the following is not a common metric for evaluating regression models?

Mean squared error
Root mean squared error
Mean absolute error
F1 score

What is the formula for calculating mean squared error?

Mean squared error = (1/n) * Σ(y_i - μ_i)^2
Mean squared error = (1/n) * Σ(y_i - μ_i)
Mean squared error = Σ(y_i - μ_i)^2
Mean squared error = Σ(y_i - μ_i)

What is the formula for calculating root mean squared error?

Root mean squared error = √sqrt((1/n) * Σ(y_i - μ_i)^2)
Root mean squared error = √sqrt((1/n) * Σ(y_i - μ_i))
Root mean squared error = √sqrt(Σ(y_i - μ_i)^2)
Root mean squared error = √sqrt(Σ(y_i - μ_i))

What is the formula for calculating mean absolute error?

Mean absolute error = (1/n) * Σ|y_i - μ_i|
Mean absolute error = (1/n) * Σ(y_i - μ_i)
Mean absolute error = Σ|y_i - μ_i|
Mean absolute error = Σ(y_i - μ_i)

Which of the following is not a common type of machine learning evaluation dataset?

Training set
Validation set
Test set
Production set

What is the purpose of a training set?

To train the machine learning model
To evaluate the performance of the machine learning model
To select the best hyperparameters for the machine learning model
To deploy the machine learning model

What is the purpose of a validation set?

To train the machine learning model
To evaluate the performance of the machine learning model
To select the best hyperparameters for the machine learning model
To deploy the machine learning model

What is the purpose of a test set?

To train the machine learning model
To evaluate the performance of the machine learning model
To select the best hyperparameters for the machine learning model
To deploy the machine learning model

Which of the following is not a common type of machine learning evaluation metric?

Accuracy
Precision
Recall
Entropy

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