FAQ on Machine Learning

PCA is unsupervised. LDA is unsupervised. PCA takes into consideration the variance. LDA takes into account the distribution of classes. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are both dimensionality reduction techniques used in machine learning, but they serve different purposes and have distinct characteristics. Here’s a brief comparison: Objective: PCA: PCA aims … Read more

If data is correlated PCA does not work well. Because of the correlation of variables the effective variance of variables decreases. Hence correlated data when used for PCA does not work well.   In the context of machine learning interview questions, if you are asked about the impact of correlation on Principal Component Analysis (PCA), … Read more

Pandas profiling is a step to find the effective number of usable data. It gives us the statistics of NULL values and the usable values and thus makes variable selection and data selection for building models in the preprocessing phase very effective.   In a machine learning interview, if you’re asked about Pandas Profiling, you … Read more

The gamma value, c value and the type of kernel are the hyperparameters of an SVM model.   C (Cost parameter): It controls the trade-off between having a smooth decision boundary and classifying the training points correctly. A smaller C value makes the decision boundary smoother, and a larger C value aims to classify all … Read more

If very few data samples are there, we can make use of oversampling to produce new data points. In this way, we can have new data points.   Data Augmentation: Augmenting your existing data by applying transformations such as rotation, scaling, flipping, or cropping can artificially increase the size of your dataset. Transfer Learning: Utilize … Read more