Questions and Answers
Predictive accuracy is defined as the ratio of true positives plus true negatives over the ______.
total
The ______ set is used for testing the model after it has been trained.
test
N-fold cross-validation involves partitioning the data into n equal-size ______.
subsets
Robustness in model evaluation refers to handling noise and ______ values.
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The ______ method involves using all but one data point for training while testing on the left out point.
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Efficiency in model evaluation includes the time to construct the model and the time to ______ the model.
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Compactness of the model can refer to the size of the tree or the number of ______.
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Training set should not be used in testing, ensuring the test set provides an ______ estimate of accuracy.
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Each fold of the cross-validation has only a single ______ example.
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A ______ set is used frequently for estimating parameters in learning algorithms.
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In classification involving skewed or highly imbalanced data, we are interested only in the ______ class.
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Accuracy is only one measure; error is calculated as 1 - ______.
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The class of interest is commonly called the ______ class.
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Cross-validation can be used for ______ estimating as well.
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In text mining, we may only be interested in the documents of a particular ______.
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A ______ matrix is used to evaluate binary classification performance.
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A computer system is said to learn from data set D to perform the task T if after learning the system’s performance on T improves as measured by ______.
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The fundamental assumption of machine learning states that the distribution of training examples is identical to the distribution of ______ examples.
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To achieve good accuracy on the test data, training examples must be sufficiently representative of the ______ data.
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In an emergency room, the problem is to predict high-risk patients and discriminate them from ______ patients.
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In a credit card application process, the application contains information such as age, marital status, and ______ rating.
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The performance measure for a loan application prediction task might be ______.
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Overfitting occurs when a model learns noise in the training data instead of the underlying ______.
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Decision trees are used for classification tasks where the goal is to predict a categorical ______.
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Study Notes
Evaluation of Classifiers
- Predictive Accuracy: Formula for accuracy is (True Positive + True Negative) / Total.
- Efficiency: Assessment based on time for model construction and time for model usage.
- Robustness: Capability to manage noise and handle missing values effectively.
- Scalability: Model's performance on large datasets in disk-resident databases.
- Interpretability: The clarity and insightfulness of the model's output.
- Compactness: Refers to the minimal size of the model, either in tree structure or rules.
Evaluation Methods
- Holdout Set: Data set is split into training (Dtrain) and test (Dtest) sets; essential to keep them disjoint for unbiased accuracy estimation.
- n-Fold Cross-Validation: Data is divided into n subsets, each serves as a test set once while the others train the model; commonly 5-fold or 10-fold used.
- Leave-One-Out Cross-Validation: Each example in a very small dataset is used as its own test case; total is equal to the number of examples (m-fold).
- Validation Set: Three subsets are created (training, validation, test); validation is for parameter tuning, often combined with cross-validation.
Classification Measures
- Accuracy Limitation: High accuracy is misleading in imbalanced scenarios; importance lies in detecting minority class effectively.
- Special Cases: In fields like text mining or fraud detection, precision and recall may be prioritized over overall accuracy.
Binary Classification: Evaluation
- Importance of confusion matrix in determining the effectiveness of binary classification, particularly in information retrieval.
Machine Learning Overview
- Machine learning involves improving task performance (T) based on data set (D) using a performance measure (M).
- Example of loan applications illustrates the value added through learning methods improving accuracy beyond the majority class approach.
Fundamental Assumption of Machine Learning
- Assumes distribution of training data mirrors that of test data, essential for classification accuracy; deviations can result in poor outcomes.
Applications of Machine Learning
- Emergency Room Example: Prioritizes patients for ICU based on survival predictions, leveraging multiple patient variables.
- Credit Card Application Example: Utilizes applicant data (age, marital status, income, debts, credit rating) to assess and predict creditworthiness.
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Description
This quiz focuses on evaluating classification methods in machine learning, emphasizing concepts such as predictive accuracy, efficiency, and robustness. Test your understanding of these key evaluation metrics and their importance in developing effective classifiers.
