False positive means it was not a spam and we called it spam, false negative means it was a spam and we didn’t label it spam Precision = (TP / TP + FP) and Recall = (TP / (TP + FN)). Increasing precision involves decreasing FP and increasing recall means decreasing FN. We don’t want…
Stemming is about replacing each word with its origin stem word in order to remove the suffixes like “es”, “ies”, “s”. For ex., “cats” => “cat”, “computers” => “computer” etc. This is more of a heuristic approach and not using any grammar or dictionary. Lemmatisation has the same purpose as above but doing it properly…
Gradient descent is the most commonly used training algorithm. Momentum is a common way to augment gradient descent such that gradient in each step is accumulated over past steps to enable the algorithm to proceed in a smoother fashion towards the minimum. RMS prop attempts to adjust learning rate for each iteration in an automated…
The batch size is a hyper parameter. Usually people try various values to see what works best in terms of speed and accuracy. Suppose you have M training instances and k batches, higher batch size is faster to do a pass on the entire dataset, through M/k mini batch iterations. As long as the data…
Ans. The commonly used loss functions are Linear : g(x) = x. This is the simplest activation function. However it cannot model complex decision boundaries. A deep network with linear activations can be shown incapable of handling non-linear decision boundaries. Sigmoid : This is a common activation function in the last layer of the neural…
Increase the number of training iterations Make a more complex network – increase hidden layer size Initialize weights to a random small value instead of zeros Change tanh activations to relu Ans : (3) . I will initialize weights to a non zero value since changing all the weights in the same…
L2 norm regularization : Make the weights closer to zero prevent overfitting. L1 Norm regularization : Make the weights closer to zero and also induce sparsity in weights. Less common form of regularization Dropout regularization : Ensure some of the hidden units are dropped out at random to ensure the network does not overfit by…
Increase number of units in the hidden layer Increase number of hidden layers Increase data set size Change activation function to tanh Try all of the above The answer is d. When I use a linear activation function, the deep neural network is realizing a linear combination of linear functions which leads to modeling only…
High bias means the data is being underfit. The decision boundary is not usually complex enough. High variance happens due to over fitting, the decision boundary is more complex than what it should be. High bias high variance happens when you fit a complex decision boundary that is also not fitting the training set…
The KNN algorithm is commonly used in many ML applications – right from supervised settings such as classification and regression, to just retrieving similar items in applications such as recommendation systems, search, question answering and so on. What is the KNN Algorithm? KNN for Nearest Neighbour Search: KNN algorithm involves retrieving the K datapoints that are…