Adaface Sample Machine Learning Questions

You are working on a regression problem using a simple neural network. You want to optimize the model's weights using gradient descent with different learning rate schedules. Consider the following pseudo code for training the neural network:

Which of the following learning rate schedules would most likely result in the fastest convergence without overshooting the optimal weights?
            
            A: Constant learning rate of 0.01
            B: Exponential decay with initial learning rate of 0.1 and decay rate of 0.99
            C: Exponential decay with initial learning rate of 0.01 and decay rate of 0.99
            D: Step decay with initial learning rate of 0.1 and decay rate of 0.5 every 100 epochs
            E: Step decay with initial learning rate of 0.01 and decay rate of 0.5 every 100 epochs
            F: Constant learning rate of 0.1

You are given a dataset to solve a classification problem using a decision tree algorithm. You are concerned about overfitting and decide to implement pruning to control the model's complexity. Consider the following pseudo code for creating the decision tree model:

Which of the following combinations of parameters would result in a less complex decision tree model, reducing the risk of overfitting?
            
            A: max_depth=5, min_samples_split=2, min_samples_leaf=1
            B: max_depth=None, min_samples_split=5, min_samples_leaf=5
            C: max_depth=3, min_samples_split=2, min_samples_leaf=1
            D: max_depth=None, min_samples_split=2, min_samples_leaf=1
            E: max_depth=3, min_samples_split=10, min_samples_leaf=10
            F; max_depth=5, min_samples_split=5, min_samples_leaf=5

Our newest machine learning developer want to write a function to calculate the n-gram of any text. An N-gram means a sequence of N words. So for example, "black cats" is a 2-gram, "saw black cats" is a 3-gram etc. The 2-gram of the sentence "the big bad wolf fell down" would be [["the", "big"], ["big", "bad"], ["bad", "wolf"], ["wolf", "fell"], ["fell", "down"]]. Can you help them select the correct function for the same?

You have trained a supervised learning model to classify customer reviews as either "positive" or "negative" based on a dataset with 10,000 samples and 35 features, including the review text, reviewer's name, and rating. The dataset is split into a 7,000-sample training set and a 3,000-sample test set.
            
            After training the model, you evaluate its performance using a confusion matrix on the test set, which shows the following results:

Based on the confusion matrix, what are the sensitivity and specificity of the model?