The difference between machine learning and statistical inference
If you’re new to the data science world, it might be hard to see a real difference between statistics and machine learning (ML). The debate on how to separate the two fields can be intense, even for those who have been in the data science community for a long time.
The truth is they are two different tools that are closely intertwined and built on a foundation of statistics. ML, at its most basic, uses statistics to make predictions based on the rules and parameters it’s been trained to follow within a dataset. Statistical inference adds depth to statistics by applying models to the data to make assumptions or infer conclusions based on relationships within the data.
But they don’t always have to work separately. In fact, ML becomes more accurate and widely available when it is enhanced with increasingly complex statistical models. Complex statistical inference can be greatly enhanced with ML tools parsing large datasets to identify relationships and patterns. When they are used together, they can bring your data analysis to a higher level.
What is machine learning?
There are many different ways to define machine learning. And there are many ways machine learning can be applied within data science. In order for us to focus on how machine learning relates to statistical inference, we’ll define it as a means of using algorithms and statistical models to analyze datasets and help data scientists organize data for analysis or make future predictions.
What is statistical inference?
In data science, statistical inference is about drawing conclusions based on your data. Statistical inference is all about making assumptions about what will happen based on statistical analysis of other datasets. You figure out the relationships in one dataset and use statistical models to speculate that those results would be similar on another comparable dataset.
There are three types of learning models that can be applied with statistical inference:
Inductive learning is about using evidence to determine the outcome. Statistics are inherently inductive—it looks at the available data, sees the most common patterns and results, and then infers the most likely outcomes on a new dataset based on the rules it established from previous datasets. It has a lot of flexibility in the types of data that can be analyzed and the predictions it can make, but it also has a greater chance of error.
Deductive learning uses general rules to determine specific outcomes. It looks at a dataset to identify those things that are always true. Then, it applies those rules to similar datasets. When the data matches, you can make a very accurate prediction. It’s built on understanding the data to see what has happened in the past and narrowing results down to only one possible outcome. It’s all about universal laws: If I do this, then this is what will happen.
Transductive learning uses specific examples to make predictions. You can train it to recognize a few different types of data, and it will analyze the entire dataset and group it based on the different data labels you trained it on, labeling and categorizing the data for you. One advantage of transductive learning is that you don’t need as much labeled data up front. A disadvantage is that it’s not making predictions for you.
How they impact your business
Each of these statistical inference learning models can be applied across your business. They can help you provide order and organization to large datasets and help you derive insights based on patterns you otherwise wouldn’t have recognized on your own.
For example, IoT devices produce massive amounts of data. If you wanted to understand how accurately your sensors monitoring cold storage temperatures across the supply chain are performing, you’d need to apply some statistical models to the data to understand what each point of data means and how it can be used to predict future outcomes.
If you wanted to use statistical models based on inductive learning, you could analyze how accurately sensors are recording temperatures across different types of sensors, different types of products being stored, and other variables that can affect the sensors. You could use inductive learning in this way:
Here is the accuracy rate of sensors installed correctly across all refrigerated trucks.
During hot weather, the accuracy rate of sensors decreases by xx%.
Based on the available data, if we add new refrigerated trucks with sensors to our fleet in the summer, we can expect a probability that the sensors will function with xx% accuracy.
If you use deductive learning, you’re typically starting with the desired result and then using the models to ensure all the evidence is met to determine the conclusion. It gives you a more narrow and focused prediction that will allow you to act with greater accuracy. For example, you could ask “What prospect leads are the most likely to convert to customers?” Deductive learning models will analyze your prospect data, identify key factors that lead to conversion, and help you prioritize leads. It would look something like the following:
This person visited your website and requested a demo.
Other people who did this became customers xx% of the time.
They should be flagged as high-priority prospects.
Transductive learning techniques would help you understand and sort the data. You can gather data from hundreds of IoT sensors, like in our example above. You will be gathering multiple data points from each sensor, and it will not be possible to know just from looking at the data what readings are accurate and which aren’t. Transductive learning would allow ML tools to parse the data and label it.
You give some examples of accurate readings.
You give some examples of inaccurate readings.
It analyzes all the data and gives you an accurate percentage rate across all sensors.
Combining statistical inference with machine learning
How you use ML and statistical inference in your business all depends on how you want to solve your business challenges. Combining both methods and techniques can create powerful tools for your predictions.
When you’re considering implementing these types of data science analysis, make sure the tools you use will enable users of all types and background experience to get the benefits of data science. These tools should be intuitive and have a wide variety of techniques and methods for you to apply to your data so you can find the right technique that will help you get the answers you need.
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