11 Most Important DS Plots

TODAY’S DAILY DOSE OF DATA SCIENCE

11 Most Important DS Plots

This visual depicts the 11 most important and must-know plots in DS:

Today, let’s understand them briefly and how they are used.

1) KS Plot:

• It is used to assess the distributional differences.
• The idea is to measure the maximum distance between the cumulative distribution functions (CDF) of two distributions.
• The lower the maximum distance, the more likely they belong to the same distribution.

2) SHAP Plot:

• It summarizes feature importance to a model’s predictions by considering interactions/dependencies between them.
• It is useful in determining how different values (low or high) of a feature affect the overall output.
• We covered model interpretability extensively in our 3-part crash course. Start here: A Crash Course on Model Interpretability →​

3) ROC Curve:

• It depicts the tradeoff between the true positive rate (good performance) and the false positive rate (bad performance) across different classification thresholds.
• The idea is to balance TPR (good performance) vs. FPR (bad performance).

4) Precision-Recall Curve:

• It depicts the tradeoff between Precision and Recall across different classification thresholds.

5) QQ Plot:

• It assesses the distributional similarity between observed data and theoretical distribution.
• It plots the quantiles of the two distributions against each other.
• Deviations from the straight line indicate a departure from the assumed distribution.

6) Cumulative Explained Variance Plot:

• It is useful in determining the number of dimensions we can reduce our data to while preserving max variance during PCA.
• Read the full article on PCA here for more clarity: Formulating the Principal Component Analysis (PCA) Algorithm From Scratch.

7) Elbow Curve:

• The plot helps identify the optimal number of clusters for the k-means algorithm.
• The point of the elbow depicts the ideal number of clusters.

8) Silhouette Curve:

• The Elbow curve is often ineffective when you have plenty of clusters.
• Silhouette Curve is a better alternative, as depicted above.

9) Gini-Impurity and Entropy:

• They are used to measure the impurity or disorder of a node or split in a decision tree.
• The plot compares Gini impurity and Entropy across different splits.
• This provides insights into the tradeoff between these measures.

10) Bias-Variance Tradeoff:

• It’s probably the most popular plot on this list.
• It is used to find the right balance between the bias and the variance of a model against complexity.

11) Partial Dependency Plots:

• Depicts the dependence between target and features.
• A plot between the target and one feature forms → 1-way PDP.
• A plot between the target and two feature forms → 2-way PDP.
• In the leftmost plot, an increase in temperature generally results in a higher target value.
• We covered model interpretability extensively in our 3-part crash course. Start here: A Crash Course on Model Interpretability →​

👉 Over to you: Which important plots have I missed here?

Thanks for reading!

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