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Sin título
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- 1What are the three types of Machine Learning?Supervised Learning, Unsupervised Learning, Reinforcement Learning
- 2—0.75
Accuracy = (TP + TN)/(TP + FN + FP + TN) - 3—0.8
Precision = TP/(TP + FP)
- 4—0.85
Recall = TP/(TP + FN)
- 5—0.88
F1 =
(2*Precision*Recall)/Precision + Recall)
- 6Leaving one data instance to validate the model on it called what?LOOVC
(stands for Leave One Out Cross-Validation) - 7k-fold cross-validation is ______________in k (linear, quadratic, exponential, cubic)Linear
- 8Machine learning algorithm uses _________ to learn a modelTraining Data
- 9Accuracy Metric is always enough to judge the Classifier (T/F)False
- 10Splitting Data into a Number of Splits and try each split separately to be the validation set is considered ______k-fold Cross Validation
- 11Define Accuracy and F1 as measures of model comparison. What generally characterizes better model performance?(No answer provided)
- 12Differentiate between K-Means and KNN algorithms?(No answer provided)
- 13Explain the concept of overfitting and underfitting of modeling in Machine Learning(No answer provided)
- 14What is confusion matrix and why do you need it?(No answer provided)
- 15—
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- Fichas sobre Sin título, creado por Jacob Christian el 04/10/2022.
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- Jacob Christian
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