1. Model-specific explanations
Congratulations on the great progress thus far! In this video, we're going to dive deeper into what makes certain AI models inherently explainable through model-specific explanations.
2. Model-specific explanations
We'll be exploring two fundamental AI systems, regression and classification. Both methods have roots that predate AI, yet they remain central in the data science landscape. They help us make sense of the vast data in today's digital world by providing clear, interpretable outcomes.
Regression is about determining the relationship between two variables, for instance the relationship between the outside temperature and ice cream sales. Classification is about predicting the label of a certain input, for instance if the fruit is red it is probably a strawberry.
3. Regression: understanding linear relationships
Let's look in more depth into regression. At its core, regression is about understanding the relationship between variables.
For instance, we might look at how outside temperature affects ice cream sales. This might seem straightforward, but here's where its explainability shines. Through regression, we can visualize this relationship. Imagine plotting temperature against ice cream sales on a graph and drawing a line that best fits the data points. This line isn't just a mathematical abstraction, it's a tangible representation of the relationship between these variables. It allows us to predict, say, ice cream sales based on temperature in an intuitive, visually interpretable way.
This visual aspect, where results can be graphed and relationships literally seen, is what makes regression inherently explainable.
4. Classification: sorting data into categories
Moving on to classification, this method is about sorting data into categories based on certain features. Think of it as sorting a basket of fruit into oranges, apples, and cucumbers based on characteristics like color and shape. The explainability of classification models often comes from their decision-making process, which, in simpler models, can be as clear as following a flowchart.
For example, a decision tree, a type of classification model, lets us trace exactly how decisions are made. First we check if the fruit is red. Next we'll check if it's round. If yes, it's probably an apple. This step-by-step process, where we can follow the logic from input to category, showcases the inherent explainability of classification models.
5. Black box models: multitude of intricate patterns
However, as we introduce more complex models that analyze a multitude of factors—like texture, genetic variety, and growth conditions—the decision-making process can become less transparent, turning these models into what we call black boxes. Despite their ability to handle vast datasets and uncover intricate patterns, the 'how' of their predictions becomes obscured.
This transition from clear, interpretable models to more opaque ones highlights the importance of XAI. As we delve further into this course, we'll explore strategies within XAI aimed at demystifying these complex models, making AI's intricate decisions more transparent and understandable.
6. Let's practice!
Now that we've clarified why regression and classification can be inherently explainable through their visual and logical clarity, let's put this understanding into practice with some exercises.