Okay, you've learned how to calculate the R-squared ( $R^2$ ) value, often called the Coefficient of Determination. But what does that number actually tell you about your regression model? Let's break down how to interpret it.

Remember, $R^2$ measures the proportion of the variance in the target variable (the one you're trying to predict) that is explained by the features used in your model. Think of it as how much of the "scatter" in the actual data points your model's prediction line accounts for.

The Scale: 0 to 1 (Usually)

$R^2$ values typically fall between 0 and 1. Here's what different values signify:

$R^2 = 1$ : This represents a perfect fit. If $R^2$ is 1, it means your model's predictions match the actual values exactly. Every single data point lies perfectly on the regression line. While this sounds ideal, in practice, achieving an $R^2$ of 1 on real-world data (especially unseen test data) is extremely rare and can sometimes indicate a problem called overfitting, where the model has learned the training data too specifically, including its noise.
$R^2 = 0$ : This indicates that your model explains none of the variability in the target variable around its mean. Essentially, the model's predictions are no better than simply guessing the average value of the target variable for all predictions. A model with $R^2 = 0$ is performing as poorly as a simple horizontal line drawn through the average of the actual values.
$0 < R^2 < 1$ : This is the most common range. The value represents the percentage of variance explained.
- An $R^2$ of 0.70 means that 70% of the variability in the target variable can be explained by the model's inputs. The remaining 30% is not captured by the model.
- An $R^2$ of 0.35 means that 35% of the variance is explained by the model.

Higher values generally indicate that the model's predictions are closer to the actual values.

Visualizing R-squared

Scatter plots can help visualize the meaning of $R^2$ . Imagine plotting your actual values against your model's predicted values:

Scatter plots comparing actual vs. predicted values for models with high, medium, and low R-squared. The dashed diagonal line represents a perfect prediction ( $Predicted = Actual$ ). Points closer to this line indicate better predictions and typically correspond to a higher $R^2$ .

Context Matters: What is a "Good" R-squared?

It's tempting to ask, "What's a good $R^2$ value?" Unfortunately, there's no single answer. The definition of a "good" $R^2$ depends heavily on the domain and the specific problem you're trying to solve:

Physics/Engineering: In controlled experiments predicting physical phenomena, you might expect very high $R^2$ values (e.g., > 0.95). The underlying relationships are often precise.
Social Sciences/Economics/Marketing: When dealing with human behavior or complex systems, the relationships are inherently noisier. An $R^2$ of 0.30 or 0.40 might be considered reasonably good, indicating the model captures a significant portion of a complex reality. Predicting stock prices, for example, is notoriously difficult, and models might have very low $R^2$ values.
Comparing Models: $R^2$ is often most useful when comparing different models for the same prediction task on the same data. A model with a higher $R^2$ generally explains more variance and might be preferred, assuming other factors are equal.

Always consider the context of your problem when interpreting $R^2$ . A value that is excellent in one field might be poor in another.

Can R-squared Be Negative?

Yes, although it's less common, $R^2$ can be negative. This happens when the model you've chosen fits the data worse than a simple horizontal line representing the mean of the target variable.

Think back to the $R^2$ formula:

R^2 = 1 - \frac{\sum_{i}(y_i - \hat{y}_i)^2}{\sum_{i}(y_i - \bar{y})^2} = 1 - \frac{MSE(\text{model})}{MSE(\text{baseline})}

Where $y_i$ are actual values, $\hat{y}_i$ are predicted values, and $\bar{y}$ is the mean of the actual values.

If the Mean Squared Error (MSE) of your model ( $\sum(y_i - \hat{y}_i)^2$ , scaled by the number of data points) is larger than the MSE of the baseline mean model ( $\sum(y_i - \bar{y})^2$ , scaled), the fraction becomes greater than 1, and $R^2$ becomes negative.

This typically signifies a very poor model fit, possibly chosen incorrectly for the data structure. You usually won't see negative $R^2$ on the training data if using standard linear regression (which minimizes squared errors), but it can occur on test data if the model generalizes poorly, or if you use models not based on minimizing squared error. A negative $R^2$ is a strong sign that the model is not suitable for the data.

In summary, interpreting $R^2$ involves understanding its scale (0 to 1 usually), relating the value to the percentage of variance explained, visualizing the fit, and most importantly, considering the context of the specific problem domain. It provides a valuable perspective on how well your regression model captures the patterns present in your data compared to simply using the average value.