Factors that make a great Machine Learning training data set
Building a training data set drives the quality of the overall machine learning model. If you are looking for some high quality data sources to build your training data sets, then read on to explore some of the useful options.
Factors that make a great Machine Learning training data set
Machine Learning (ML) is a
process of induction.
ML heavily rely on training
datasets to train their model
and ‘learn’ better.
Training data helps train the ML
program for building a particular
type of modeling.
Once this is done, it is passed
through actual data that it hasn’t
been trained on, using test dataset.
Hence test dataset is the data for
which the ML program was trained
using the training dataset.
Both training and test datasets
will try to align to representative
This ensures that the outcomes
will be universally applicable for
If you are looking for some high
quality data sources to build your
training datasets, then read on to
explore some of the useful
1. UCI- Machine Learning repository
2. Iris by UCI
5. ML Bench by R
9. DataStock by PromptCloud
What factors are to
be considered when
We’ve listed some of them below.
1. The right quantity
You need to assess and have an answer ready for these
basic questions around the quantity of data
• The number of records to take from the databases
• The size of the sample needed to yield expected
• The split of data for training and testing or use an
alternate approach like k-fold cross validation
Jeff Dean, the head of the
Google Brain project stated that
deep learning takes at least
2. The approach to splitting data
• You need data to build the model, and you need
data to test the model.
• There should be a method to split the dataset into
these two portions. You can go for random split or
time based split.
• In the latter, the general rule of thumb is that older
data is for training and newer data is for testing.
Some datasets need other approaches like
stratified sampling or clustered sampling.
If you really aren’t sure, do a small pilot to
validate your model and then roll it full-
fledged across the board.
3. The past
• You can check out studies that have problems
similar to your current problem and take the data
for better efficacy of the model building process.
• If you are fortunate enough to get a big number of
similar studies carried out in the past, you can
average out over them for your building purposes.
4. Domain expertise
• Typically, the samples you feed in need to possess
two key qualities – independence and identical
• To determine the quality of data, have a subject
matter expert run a trained pair of eyes through
• The expert can also help to simulate data that you
don’t have currently but wish to use to train the
machine learning program.
5. The right kind of data
• Once you have processed the clean data, you can
transform it based on your machine learning
• This step of feature engineering helps in
transforming the data into one best suited for a
particular type of analysis.
Feature engineering can comprise one or
more of the following data transformation
• Normally a processed dataset will have attributes
that use a variety of scales for metrics such as
weights (kilograms or pounds), distance
(kilometers or miles), or currency (dollars or
• You will need to reduce the variations in the scale
for a much better result.
• With the help of functional decomposition, a
complex variable can be split into granular level
into its constituent parts.
• These individual constituent parts may have some
inherent properties or characteristics that can
augment in the entire machine learning building
• It helps to separate the ‘noise’ from the elements
or components you are actually interested in for
building the training datasets.
The way a Bayesian network method tries
to split a joint distribution along its causal
fault line, is a classic example of
decomposition at work.
• It combines multiple variables featuring similar
attributes into a single bigger entity.
• For some machine learning datasets, this may be a
more sensible way to build the dataset for solving
a particular problem.
An example can be how aggregate survey
responses can be tracked rather than looking
at individual responses, to solve a particular
problem through machine learning.
Identifying the type of algorithm
• Knowing what type of algorithm you are running
after, you will be able to better assess the type and
quantity of data needed for building the training
• You can go for a linear or a non-linear algorithm.
• Typically, non-linear algorithms are considered
more powerful. They are able to grasp and
establish connections in non-linear
relationships between the input and output
• They can figure out not only how many
parameters are required but also determine
what values to be present for these parameters
to better resolve a specific machine learning
• This also means that non-linear algorithm needs
much more volume of data inside the training
dataset for it to grasp the complex connections
and relationships between different entities
• Most of the better known enterprises are
interested in such algorithms that keep
improving as more and more data is input into
Identifying correctly ‘if’ and
‘when’ big data is required
• When you're building a training dataset, you need
to assess smartly if at all big data (very high volume
of data) is needed.
• If so, then at what point of the dataset creation,
should we bring in the big data.
• A classic example is when you are carrying out
traditional predictive modeling, you may reach a
point of diminishing returns where the yields
will not correspond to the amount of data you
have input. You may need far more data to
overcome this barrier.
• By carefully assessing your chosen model and
your specific problem in hand, you can figure
out when this point will arrive and when you
would need a much bigger volume of data.
• Building a training dataset drives the quality of the
overall machine learning model.
• With these factors, you can make certain that you
build a high performance machine learning dataset
and reap the benefit of a robust, meaningful, and
accurate machine learning model that has ‘learnt’
from such a superior training dataset.
Are you looking to acquire web data for
Let us know your requirements at