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Questions and Answers
What is the basis of a Naive Bayes classifier?
Which step is involved in the classification process based on the provided data?
In the provided student data, what does a total score of +1 indicate?
How is accuracy calculated in the given classification process?
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In probability theory, what does a probability value close to 1 signify?
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Which of the following statements accurately describes conditional probability?
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What assumption does the Naive Bayes classifier make about the features used in the model?
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In the provided student data, what was the accuracy of the classifier's predictions?
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What type of learning is associated with a training set where each row has a predefined class label?
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Why is the predictive accuracy of a classifier likely to be optimistic if measured using the training set?
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What is the main purpose of using a test set in the classification process?
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Which of the following correctly describes the class label attribute?
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Which term is NOT synonymous with data rows in the context of classification?
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What is meant by the term 'overfitting' in the context of classifiers?
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If a classifier has a 90% accuracy on a test set, what does this indicate?
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Which aspect is NOT a characteristic of the rows in a training set?
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Which machine learning technique is used to identify unknown patterns in data with minimal human supervision?
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Which type of classification problem involves the target attribute having only two possible values?
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Which machine learning technique predicts continuous-valued functions?
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Which method can be used to predict how much a customer will spend during a sale at a computer store?
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Which example best represents a multiclass classification problem?
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Which unsupervised learning method is used to group data points with similar characteristics?
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What is the primary goal of supervised machine learning techniques like classification and prediction?
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Which machine learning technique would you use if you don't have a specific goal but want to find hidden relationships in data?
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Study Notes
Classification Process
- The accuracy of a classifier is determined by comparing the class label of each test row with the predicted class label.
- If the accuracy is acceptable, the classifier can be used to classify future data rows with unknown class labels.
Naïve Bayes Classification
- Naïve Bayes is a supervised machine learning algorithm used for classification tasks.
- It is based on Bayes' Theorem with an assumption of independence among predictors.
- The algorithm assumes that features are independent of each other and changing one feature does not directly influence another.
Probability
- Probability is a branch of mathematics that deals with numerical descriptions of event likelihood.
- Probability values range from 0 (impossibility) to 1 (certainty).
- Conditional probability is a key concept in Naïve Bayes classification.
Supervised Machine Learning
- Supervised learning involves training a classifier on a labeled dataset.
- The class label attribute is nominal-valued and categorical.
- Training rows are selected from a database for analysis.
- Supervised learning is used for classification and prediction tasks.
Classification vs. Prediction
- Classification predicts categorical labels, while prediction models continuous-valued functions.
- Binary classification involves target attributes with two possible values, while multiclass targets have more than two values.
Unsupervised Machine Learning
- Unsupervised learning involves finding unknown patterns in a dataset with no predetermined labels.
- It is used when a specific goal is not available or when the user seeks to find hidden relationships in data.
- Unsupervised learning methods include clustering, association, and extraction methods.
Examples of Classification and Prediction
- Classification examples: loan applicant risk assessment, customer purchase prediction, and medical treatment prediction.
- Prediction examples: predicting customer spending, price of gasoline, rice, or USD.
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Description
Learn about the classification process in machine learning, including the training phase and class labels. Understand how the classifier is built and how each row is assigned a predefined class.