Types of Missing Data

BySourabh Gupta

Below are the different types of missing data generally found in machine learning problems

1. MCAR(Missing completely at random)

These values do not depend on any other features. In this case, there may be no pattern as to why a column’s data is missing. For example, survey data is missing because someone could not make it to an appointment, or an administrator misplaces the test results he is supposed to enter into the computer. The reason for the missing values is unrelated to the data in the dataset.

2. MAR(Missing at random)

These values may be dependent on some other features. In this scenario, the reason the data is missing in a column can be explained by the data in other columns. For example, a school student who scores above the cutoff is typically given a grade. So, a missing grade for a student can be explained by the column that has scored below the cutoff. The reason for these missing values can be described by data in another column.

3. MNAR(Missing not at random)

These missing values have some reason for why they are missing. Sometimes, the missing value is related to the value itself. For example, higher-income people may not disclose their incomes. Here, there is a correlation between the missing values and the actual income. The missing values are not dependent on other variables in the dataset.

Similar Posts

|

 II. Multicollinearity – VIF

BySourabh Gupta

Multicollinearity is a phenomenon unique to multiple regressions that occurs when two variables that are supposed to be independent in reality have a high amount of correlation and are overlapping in what they measure.  In other words, each variable doesn’t give you entirely new information. To picture what multicollinearity is, let’s start by picturing what it…

ROC Curve and AUC for Model Selection

Byvaishanavi vaishanavi

Classifiers generally don’t return a simple “Yes-or-No” answer. Mostly, a classification procedure will return a score along a range as shown in below example: We know that logistic regression gives us the result in the form of probability. Say, we are building a logistic regression model to detect whether breast cancer is malignant or benign. A model…

Everything you need to know about Reinforcement Learning

ByAbhinav Bandaru

The phrase “Reinforcement Learning” could sound a little intimidating at first, but when we break it down, it’s actually quite simple. Let’s start with the phrase itself. What does the word “reinforce” mean? No! don’t get googling already! I’ll tell you. It simply means to strengthen or support something. So Reinforcement Learning would mean, strengthening…

|

ANOVA

BySourabh Gupta

Buying a new product or testing a new technique but not sure how it stacks up against the alternatives? It’s an all too familiar situation for most of us. Most of the options sound similar to each other so picking the best out of the lot is a challenge. In order to make a confident…

Leave a Reply

Your email address will not be published.