Datasets: Data characteristics

A dataset is a collection of examples.

Many datasets store data in tables (grids), for example, as comma-separated values (CSV) or directly from spreadsheets or database tables. Tables are an intuitive input format for machine learning models. You can imagine each row of the table as an example and each column as a potential feature or label. That said, datasets may also be derived from other formats, including log files and protocol buffers.

Regardless of the format, your ML model is only as good as the data it trains on. This section examines key data characteristics.

Types of data

A dataset could contain many kinds of datatypes, including but certainly not limited to:

• numerical data, which is covered in a separate unit
• categorical data, which is covered in a separate unit
• human language, including individual words and sentences, all the way up to entire text documents
• multimedia (such as images, videos, and audio files)
• outputs from other ML systems
• embedding vectors, which are covered in a later unit

Quantity of data

As a rough rule of thumb, your model should train on at least an order of magnitude (or two) more examples than trainable parameters. However, good models generally train on substantially more examples than that.

Models trained on large datasets with few features generally outperform models trained on small datasets with a lot of features. Google has historically had great success training simple models on large datasets.

Different datasets for different machine learning programs may require wildly different amounts of examples to build a useful model. For some relatively simple problems, a few dozen examples might be sufficient. For other problems, a trillion examples might be insufficient.

It's possible to get good results from a small dataset if you are adapting an existing model already trained on large quantities of data from the same schema.

Quality and reliability of data

Everyone prefers high quality to low quality, but quality is such a vague concept that it could be defined many different ways. This course defines quality pragmatically:

A high-quality dataset helps your model accomplish its goal. A low quality dataset inhibits your model from accomplishing its goal.

A high-quality dataset is usually also reliable. Reliability refers to the degree to which you can trust your data. A model trained on a reliable dataset is more likely to yield useful predictions than a model trained on unreliable data.

In measuring reliability, you must determine:

• How common are label errors? For example, if your data is labeled by humans, how often did your human raters make mistakes?
• Are your features noisy? That is, do the values in your features contain errors? Be realistic—you can't purge your dataset of all noise. Some noise is normal; for example, GPS measurements of any location always fluctuate a little, week to week.
• Is the data properly filtered for your problem? For example, should your dataset include search queries from bots? If you're building a spam-detection system, then likely the answer is yes. However, if you're trying to improve search results for humans, then no.

The following are common causes of unreliable data in datasets:

• Omitted values. For example, a person forgot to enter a value for a house's age.
• Duplicate examples. For example, a server mistakenly uploaded the same log entries twice.
• Bad feature values. For example, someone typed an extra digit, or a thermometer was left out in the sun.
• Bad labels. For example, a person mistakenly labeled a picture of an oak tree as a maple tree.
• Bad sections of data. For example, a certain feature is very reliable, except for that one day when the network kept crashing.

We recommend using automation to flag unreliable data. For example, unit tests that define or rely on an external formal data schema can flag values that fall outside of a defined range.

Note: Any sufficiently large or diverse dataset almost certainly contains outliers that fall outside your data schema or unit test bands. Determining how to handle outliers is an important part of machine learning. The Numerical data unit details how to handle numeric outliers.

Complete vs. incomplete examples

In a perfect world, each example is complete; that is, each example contains a value for each feature.

Figure 1. A complete example.

 

Unfortunately, real-world examples are often incomplete, meaning that at least one feature value is missing.

Figure 2. An incomplete example.

 

Don't train a model on incomplete examples. Instead, fix or eliminate incomplete examples by doing one of the following:

• Delete incomplete examples.
• Impute missing values; that is, convert the incomplete example to a complete example by providing well-reasoned guesses for the missing values.

Figure 3. Deleting incomplete examples from the dataset.

 

Figure 4. Imputing missing values for incomplete examples.

 

If the dataset contains enough complete examples to train a useful model, then consider deleting the incomplete examples. Similarly, if only one feature is missing a significant amount of data and that one feature probably can't help the model much, then consider deleting that feature from the model inputs and seeing how much quality is lost by its removal. If the model works just or almost as well without it, that's great. Conversely, if you don't have enough complete examples to train a useful model, then you might consider imputing missing values.

It's fine to delete useless or redundant examples, but it's bad to delete important examples. Unfortunately, it can be difficult to differentiate between useless and useful examples. If you can't decide whether to delete or impute, consider building two datasets: one formed by deleting incomplete examples and the other by imputing. Then, determine which dataset trains the better model.

Click the icon to learn more about imputation handling.

Clever algorithms can impute some pretty good missing values; however, imputed values are rarely as good as the actual values. Therefore, a good dataset tells the model which values are imputed and which are actual. One way to do this is to add an extra Boolean column to the dataset that indicates whether a particular feature's value is imputed. For example, given a feature named temperature, you could add an extra Boolean feature named something like temperature_is_imputed. Then, during training, the model will probably gradually learn to trust examples containing imputed values for feature temperature less than examples containing actual (non-imputed) values.

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Imputation is the process of generating well-reasoned data, not random or deceptive data. Be careful: good imputation can improve your model; bad imputation can hurt your model.

One common algorithm is to use the mean or median as the imputed value. Consequently, when you represent a numerical feature with Z-scores, then the imputed value is typically 0 (because 0 is generally the mean Z-score).

Exercise: Check your understanding

A sorted dataset, like the one in the following exercise, can sometimes simplify imputation. However, it is a bad idea to train on a sorted dataset. So, after imputation, randomize the order of examples in the training set.

Here are two columns of a dataset sorted by Timestamp.

Timestamp	Temperature
June 8, 2023 09:00	12
June 8, 2023 10:00	18
June 8, 2023 11:00	missing
June 8, 2023 12:00	24
June 8, 2023 13:00	38

Which of the following would be a reasonable value to impute for the missing value of Temperature?

23

Probably. 23 is the mean of the adjacent values (12, 18, 24, and 38). However, we aren't seeing the rest of the dataset, so it is possible that 23 would be an outlier for 11:00 on other days.

31

Unlikely. The limited part of the dataset that we can see suggests that 31 is much too high for the 11:00 Temperature. However, we can't be sure without basing the imputation on a larger number of examples.

51

Very unlikely. 51 is much higher than any of the displayed values (and, therefore, much higher than the mean).

Key terms:

• Dataset
• Embedding vector
• Example
• Feature
• Model
• Value imputation
• Z-score normalization