डेटा में अनिश्चितता की माप करने वाली एंट्रॉपी (Entropy) के संबंध में निम्नलिखित में से कौन सा/से कथन सही है/हैं? I. कम एंट्रॉपी यह दर्शाती है कि डेटा लेबल पूरी तरह से समान रूप से वितरित हैं। II. उच्च एंट्रॉपी का अर्थ है कि लेबल अव्यवस्थित, अनिश्चित या मिश्रित हैं।

Given below are two statements: one is labelled as Assertion A and the other is labelled as Reason R. Assertion A: The Naive Bayes classifier is defined as a 'lazy learner' that avoids the training phase by assuming that all features in a dataset are strongly dependent on each other. Reason R: The Naive Bayes algorithm remains highly effective for complex tasks like text classification and spam filtering despite its 'naive' presumption of conditional feature independence. In the light of the above statements, choose the most appropriate answer from the options given below:

Consider the following statements regarding continuous prediction models in regression: Statement 1: Linear Regression utilizes the Ordinary Least Squares (OLS) method to model linear relationships by minimizing the sum of the squared differences between observed data points and predicted values. Statement 2: Polynomial Regression models non-linear relationships by utilizing a sigmoid function to transform higher-degree features into a probability range between 0 and 1. Which of the statements given above is/are correct?