1What is a confusion matrix?
A confusion matrix is a method used to evaluate the performance of a classification model by measuring its correctness.
2What are the four variables in a confusion matrix?
True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN).
3What does a True Positive (TP) represent?
TP is when the model correctly predicts a positive class, like predicting a dog, and the actual outcome is also a dog.
4What is a False Positive (FP)?
FP is when the model predicts a positive class incorrectly, like predicting a dog when the actual outcome is a cat. This is also called Type I error.
5What is a False Negative (FN)?
FN is when the model fails to predict a positive class, like predicting not a dog when the actual outcome is a dog. This is also called Type II error.
6What is accuracy in the context of a confusion matrix?
Accuracy is the proportion of correct predictions out of the total predictions, calculated as (TP + TN) / (TP + TN + FP + FN).
7What is precision in the context of classification?
Precision is the proportion of true positives out of all predicted positives, calculated as TP / (TP + FP).
8What is sensitivity or recall?
Sensitivity (or recall) is the proportion of actual positives that were correctly predicted, calculated as TP / (TP + FN).
9What is the F1 score?
The F1 score is a balanced measure of accuracy, calculated as 2TP / (2TP + FP + FN), harmonizing precision and recall.
10What is the k-NN algorithm based on?
The k-NN algorithm is based on the assumption that similar things exist in close proximity, classifying data based on its nearest neighbors.
11What is the main disadvantage of the k-NN algorithm?
The main disadvantage of k-NN is its high computational cost, especially when calculating distances between all training data points.
12What is the curse of dimensionality?
The curse of dimensionality refers to the exponential increase in modeling error as the number of features or dimensions in the data increases, making the model lose predictive power.
13What technique helps reduce the curse of dimensionality?
Dimensionality reduction is a technique that helps minimize the curse of dimensionality by reducing the number of features used in modeling.
14What happens if the value of "k" in k-NN is too low?
If "k" is too low (like k=1 or k=3), the model can be noisy due to the influence of outliers.
15What happens when "k" in k-NN is too high?
If "k" is too high, it can lead to computational difficulties and loss of model effectiveness.
16What is the preferred value for "k" in the k-NN algorithm?
The preferred value for "k" is usually 5, as it's a good balance between model accuracy and computational efficiency.
These flashcards cover key concepts from the presentation on the confusion matrix and the k-NN algorithm.