FiveKey Trends Shaping The Future Of Predictive Analytics
Determining A (Data) Model Of Excellence
Predictive analytics can be derived from a variety of data
models depending on the type of data available. When deciding which model to
use, there are many factors organizations consider, such as the resources it
will take to develop the model and how accurate the model needs to be. Seven models
and techniques commonly used for predictive analytics include:
1. Machine learning and AI
models - Where more traditional statistical models were previously the
norm for predictive analytics, ML- and AI-based models have gained significant
traction in recent years due to their accuracy and their ability to be deployed
by professionals who may not be highly trained statisticians.
2. Time series data models - Models
involving time series data analyze temporal or time-stamped data to forecast
future values. These models are particularly effective for data with time-based
dependencies such as seasonal variations, although they may not be as effective
if the data isn't collected at regular intervals.
3. Regression models - Widely
used for instances such as predicting stock returns or home prices, regression
models are effective when there is a need to identify a clear relationship
between variables. That said, these models can struggle when there are too many
variables, and they do require a relatively high level of statistical knowledge
for full effectiveness.
4. Decision tree models - Think
of a decision tree model as an "if this, then that" situation where
the model makes predictions by learning basic rules from the data. These models
can provide easy-to-understand results, but their functionality can be fragile
if there are changes to the data.
5. Gradient boosting models
- Rather than building one powerful model, gradient boosting
involves multiple simpler models that are stronger when combined. This is an
effective method when making predictions for non-linear data, but the caveat is
the models need proper and consistent tuning.
6. Random forest models - Similar
to gradient boosting, random forest models use a combination of simpler models,
mainly decision trees. This allows for the analysis of each individual tree's
prediction while also creating an aggregate final prediction.
7. Clustering models - Clustering
is often used to group data points together, but it can also be used as inputs
for predictive models. Clustering models are useful when identifying hidden
relationships or patterns in the data but also require a way to determine how
similar the data points are.
Again, the data model or models an organization uses will
largely depend on the data they have at their disposal and the results they’d
like to achieve.
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