एक विशिष्ट मशीन लर्निंग वर्कफ़्लो में निम्नलिखित चरणों पर विचार कीजिए: A. मॉडल प्रशिक्षण (Model training) B. मॉडल मूल्यांकन (Model evaluation) C. डेटा प्रीप्रोसेसिंग (Data preprocessing) D. मॉडल परिनियोजन (Model deployment) नीचे दिए गए विकल्पों में से चरणों का सही क्रम चुनिए:

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?