1 & 2
2 & 3
3 & 4
all of the above
Answer is Right!
Answer is Wrong!
The correct answer is D. all of the above.
PCA is an unsupervised learning method that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. The number of principal components is less than or equal to the number of original variables. The first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to (i.e., uncorrelated with) the preceding components.
Here is a brief explanation of each option:
- PCA is an unsupervised method: This means that PCA does not require any labels or class information about the data. It can be used to find patterns in data without any prior knowledge about the data.
- It searches for the directions that data have the largest variance: PCA finds the directions in the data that have the most variance. This means that the principal components will capture the most variation in the data.
- Maximum number of principal components <= number of features: The number of principal components is always less than or equal to the number of features in the data. This is because each principal component is a linear combination of the features, and there can only be as many linear combinations as there are features.
- All principal components are orthogonal to each other: This means that the principal components are perpendicular to each other. This is a property of the orthogonal transformation that PCA uses.
I hope this helps!