Machine Learning: Analytical Learning

1. Introduction

In this tutorial, we’ll talk about analytical learning in machine learning. We’ll present how analytical learning works and discuss its methods. We’ll talk also about the scientific concepts in analytical learning.

2. What Are the Problems With Inductive Learning in Machine Learning?

Inductive learning and analytical learning are the two categories into which machine learning methods can be classified, according to Thrun (1995). Inductive learning, or discovery learning, is the process in which learners find patterns and rules only by processing examples.

One problem faced by inductive learning is bias, closely coupled with a limited sample size. For example, if someone is trying to identify different types of plants in a forest by only observing a small area, they may miss certain species or make incorrect assumptions about the characteristics of those plants based on limited data.

Another issue arises from this, and that’s the potential for overgeneralization. The danger is that a learner may assume that all members of a group share certain characteristics based on a limited number of observations. For instance, if someone observes a few blue jays with red feathers, they may assume that all blue jays have red feathers, which isn’t true

Thus, analytical learning comes to solve the problem of limited data.

3. How Does Analytical Learning Work?

Analytical learning uses prior knowledge to guide the learning process.

3.1. Simple Example of Analytical Learning

Let’s say we are trying to learn how to identify different types of birds. In the case of a bird classifier, we might manually select some features based on theory. For example, we might know that birds have feathers and wings, so we would include features related to these traits in our model.

We could also incorporate rules into the cost function. For example, we might know that certain types of birds tend to have certain colors or patterns, so we could penalize our model for misclassifying a bird based on its color or pattern.

Finally, we could restrict our search to specific models based on our prior knowledge. For example, if we know that birds can be classified into different families based on their beak shape, we might only consider models that include features related to beak shape.

This is what analytical learning does. It uses prior knowledge to guide the machine learning process rather than relying solely on the data to determine the best model.

3.2. The Process of Analytical Learning

Analytical learning requires models to fit both prior knowledge and data:

1. We use prior knowledge to select the model family and features or make a problem-specific cost function.
2. The search process uses the available data to find a model.
3. When testing a model, we require that it fits both data and theory.

Once a satisfactory solution is found, it can be further refined and improved using additional data or feedback.

Analytical learning is particularly useful in situations where the available data is limited or noisy. It’s also useful when there are complex relationships between features, and a purely inductive approach might not have enough power to reveal them without the help of prior knowledge.

4. Benefits and Shortcomings of Analytical Learning

4.1. Benefits of Analytical Learning

Analytical learning comes with several benefits:

• Improved accuracy: it can improve the accuracy of predictions by incorporating prior knowledge and reducing the impact of noise or outliers in the data
• Faster learning: by leveraging prior knowledge, analytical learning can reduce the amount of data required to construct accurate classifiers or regressors, which speeds up the learning process
• Better generalization: analytical learning can help models generalize better to new data by incorporating domain-specific knowledge and avoiding overfitting the training data
• Increased interpretability: we get more interpretable models because they’re based on human expertise and domain knowledge

4.2. Shortcomings of Analytical Learning

However, it also has some shortcomings:

• Assumption of prior knowledge: analytical learning assumes that prior knowledge is available, which may not always be the case. This can limit the applicability of analytical learning to only those domains with sufficiently developed theories.
• Difficulty in handling complex relationships: analytical learning methods may struggle to handle complex relationships between variables, especially when the data is high-dimensional or non-linear. This can limit their ability to accurately model certain types of data.
• Requires expertise: Analytical learning requires a certain level of expertise and knowledge in a particular field, which may not be accessible to everyone.

5. Analytical vs. Inductive Learning

The two main differences between analytical and inductive learning methods are the use of prior knowledge and data requirements:

6. Conclusion

In this article, we talked about analytical learning.

Overall, analytical learning involves using existing knowledge and experience to guide the search for a machine-learning model rather than relying solely on data.