Machine Learning vs. Traditional Analytics - What’s changed?

Last Updated: April 2023

Everyone is talking about Machine Learning these days but how exactly is that different from the type of analysis that companies have been performing historically?

Knowing how to use AI often requires a bit of a mental shift away from how we traditionally think about data. If we don’t let go of the previous paradigm, it will limit what AI can do for us. So, here goes…

Machine Learning vs. Traditional Analysis

Traditional analysis typically involves applying statistical methods to a set of data in order to derive insights and make predictions. This approach requires the analyst to have a deep understanding of the underlying mathematical principles and assumptions of the statistical models being used.

Machine learning, on the other hand, is a subset of artificial intelligence that focuses on enabling computers to learn from data without being explicitly programmed. Machine learning algorithms are designed to automatically identify patterns in data and use those patterns to make predictions or decisions.

The key difference between traditional analysis and machine learning is that traditional analysis requires the analyst to identify the statistical models to be used and to manually define the inputs and outputs for the analysis. Machine learning, on the other hand, involves the use of algorithms that automatically learn the relationships between inputs and outputs from data.

Another important difference is that traditional analysis tends to work well with smaller data sets that have well-defined relationships, while machine learning is often better suited to larger and more complex data sets with many interrelated variables.

Overall, traditional analysis is a useful approach when the underlying relationships in the data are well-understood and can be easily modeled using statistical techniques. Machine learning, on the other hand, is ideal when the relationships in the data are more complex or difficult to discern, or when the data set is too large for traditional analysis methods.

Types of Machine Learning

Some people confuse Machine Learning with Neural Networks (yes, I’m referring to myself : ) )…. So to set things straight these are the most commonly used types of machine learning of which neural networks is only one!

Some of these don’t require the crazy computational power that neural networks require and maybe benefit from being more interpretable.

  1. Decision trees: Decision trees are a type of supervised learning algorithm that can be used for both classification and regression tasks. They work by recursively partitioning the data into smaller and smaller subsets based on the values of the input features, ultimately leading to a set of if-then rules that can be used to make predictions.
  2. Random forests: Random forests are an ensemble method that combines multiple decision trees to improve the accuracy and robustness of the predictions. The algorithm creates a set of decision trees, each trained on a randomly sampled subset of the data and a randomly selected subset of the input features. The final prediction is made by aggregating the predictions of all the individual trees.
  3. Support vector machines (SVM): SVMs are a type of supervised learning algorithm that can be used for both classification and regression tasks. They work by finding a hyperplane that maximally separates the examples of different classes in the input space. SVMs are particularly useful when the data is not linearly separable, as they can use kernel functions to map the data into a higher-dimensional feature space where linear separation is possible.
  4. K-nearest neighbor (KNN): KNN is a simple, non-parametric algorithm that can be used for both classification and regression tasks. It works by finding the K examples in the training data that are closest to a new, unseen example in terms of some distance metric, and making the prediction based on the labels or values of those examples.
  5. Neural networks: Neural networks are a type of machine learning algorithm that are designed to mimic the structure and function of the human brain. They consist of interconnected nodes or neurons that are organized into layers and can learn from large amounts of data to make predictions or decisions. There are many types of neural networks, including feedforward networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and more. Neural networks have been shown to be particularly effective for tasks such as image recognition, speech recognition, and natural language processing.