每日一练 | Data Scientist & Business Analyst & Leetcode 面试题 344

大数据应用 · 公众号 · 大数据 · 2018-04-28 09:55

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APR

Data Application Lab 自2017年6月15日起，每天和你分享讨论一道数据科学(DS)和商业分析(BA) 领域常见的面试问题。

自2017年10月4日起，每天再为大家分享一道Leetcode 算法题。

希望积极寻求相关领域工作的你每天关注我们的问题并且与我们一起思考，我们将会在第二天给出答案。

Day

244

DS Interview Question

Give me some examples about the applications of Naive Bayes Algorithms.

BA Interview Question

What is the difference between UNION and UNION ALL in SQL.

LeetCode Question

Subsets

Description:

Given a set of distinct integers, nums, return all possible subsets.

Input: [1,2,3]

Output: [[],[1],[1,2],[1,2,3],[1,3],[2],[2,3],[3]]

Assumptions:

The solution set must not contain duplicate subsets.

Day

243

答案揭晓

DS Interview Question & Answer

What are the Pros and Cons of Naive Bayes?

Answer:

Pros:

- It is easy and fast to predict class of test data set. It also perform well in multi class prediction.

- When assumption of independence holds, a Naive Bayes classifier performs better comparing to other models like logistic regression and you need less training data.

- It perform well in case of categorical input variables compared to numerical variable(s). For numerical variable, normal distribution is assumed (bell curve, which is a strong assumption).

Cons:

- If categorical variable has a category (in test data set), which was not observed in training data set, then model will assign a 0 (zero) probability and will be unable to make a prediction. This is often known as “Zero Frequency”. To solve this, we can use the smoothing technique. One of the simplest smoothing techniques is called Laplace estimation.

- On the other side naive Bayes is also known as a bad estimator, so the probability outputs from predict_proba are not to be taken too seriously.

- Another limitation of Naive Bayes is the assumption of independent predictors. In real life, it is almost impossible that we get a set of predictors which are completely independent.