Introduction to Data Science: A Case Study
Welcome to the Introduction to Data Science Quiz! This quiz is about data science. It is intended for beginners, with no specific knowledge of data science, statistics or maths. Common sense and reasoning should be your core strengths to answer the quiz. In this quiz, you will go through a case study supported with an actual dataset. The questions will guide you through the main steps of the analysis. You will be asked to evaluate results, confirm or infirm assumptions or validate the understanding of concepts previously introduced. Questions are grouped into sections, describing the main steps of a data science project. For each question, you will be provided with a screenshot of text and figures. The text will introduce the concept of the question and might provide tips to answer it. The figures will illustrate the text and may also be used to answer questions.
1. Educational explanations. 👍 Explanations cover why the correct answers are correct and why the
wrong answers are wrong. E.g.: #q1, #q2, #q3, etc.
2. Provides a valuable service, teaches something useful that can be used in real life. 👍
- each ques is paragraph type which makes it different from other quizzes.
- quality of questions are also good.
- moreover I didn't find any copyright violations although this can be checked by others for surety.
- long quiz.
Question 19 : Why is this model naïve? - Because it predicts the same value for all samples, without considering the values of the samples
But as far I know it is called naive because it makes strong assumption that features are independence of each other.
i understand your point, but this question does not refer to naive bayes model. Here, the naive model simply computes the average value of the target on the training set; it is not a naive bayes classifier.
It is naive because it predicts the same output (the average target), no matter the input fed to it. As such, and as it is described in the following link (first paragraph), it can be considered a naive model.