How Artificial Intelligence Deals with Missing Data
To answer a given question correctly, you must have all the facts. You can guess the answer to a question without all the facts, but then the answer is just as likely to be wrong as correct. Often, someone who makes a decision, essentially answering a question, without all the facts is said to jump to a conclusion. When analyzing data, you have probably jumped to more conclusions than you think because of missing data. A data record, one entry in a dataset (which is all the data), consists of fields that contain facts used to answer a question. Each field contains a single kind of data that addresses a single fact. If that field is empty, you don’t have the data you need to answer the question using that particular data record.
As part of the process of dealing with missing data, you must know that the data is missing. Identifying that your dataset is missing information can actually be quite hard because it requires you to look at the data at a low level — something that most people aren’t prepared to do and is time consuming even if you do have the required skills. Often, your first clue that data is missing is the preposterous answers that your questions get from the algorithm and associated dataset. When the algorithm is the right one to use, the dataset must be at fault.
A problem can occur when the data collection process doesn’t include all the data needed to answer a particular question. Sometimes you’re better off to actually drop a fact rather than use a considerably damaged fact. If you find that a particular field in a dataset is missing 90 percent or more of its data, the field becomes useless, and you need to drop it from the dataset (or find some way to obtain all that data).
Less damaged fields can have data missing in one of two ways. Randomly missing data is often the result of human or sensor error. It occurs when data records throughout the dataset have missing entries. Sometimes a simple glitch will cause the damage. Sequentially missing data occurs during some type of generalized failure. An entire segment of the data records in the dataset lack the required information, which means that the resulting analysis can become quite skewed.
Fixing randomly missing data is easiest. You can use a simple median or average value as a replacement. No, the dataset isn’t completely accurate, but it will likely work well enough to obtain a reasonable answer. In some cases, data scientists used a special algorithm to compute the missing value, which can make the dataset more accurate at the expense of computational time.
Sequentially missing data is significantly harder, if not impossible, to fix because you lack any surrounding data on which to base any sort of guess. If you can find the cause of the missing data, you can sometimes reconstruct it. However, when reconstruction becomes impossible, you can choose to ignore the field. Unfortunately, some answers will require that field, which means that you might need to ignore that particular sequence of data records — potentially causing incorrect output.