9 Most Important Plots in Data Science

...in a single frame

Exploring and analyzing data is a fundamental aspect of data science.

Here, visualizations play a crucial role in understanding complex patterns and relationships.

They offer a concise way to:

• understand the intricacies of statistical models,

• validate model assumptions,

• evaluate model performance, and much more.

The visual above depicts 9 of the most important and must-know plots in data science.

• KS Plot: It compares the cumulative distribution functions (CDFs) of a dataset to a theoretical distribution or between two datasets to assess the distributional differences.

• SHAP Plot: It provides a summary of feature importance to a model’s predictions, by considering interactions/dependencies between them.

• QQ Plot: It is used to assess the distributional similarity between observed data and theoretical distribution.

  • Here, we plot the quantiles of the two distributions against each other.

  • Deviations from the straight line indicate a departure from the assumed distribution.

• Cumulative Explained Variance Plot: I covered this in a detailed post before: How Many Dimensions Should You Reduce Your Data To When Using PCA?

• Gini-Impurity vs. 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.

• Bias-Variance Tradeoff: It is used to find the right balance between the bias and the variance of a model.

• ROC Curve: It depicts the trade-off between the true positive rate (TPR) and the false positive rate (FPR) across different classification thresholds.

• Precision-Recall Curve: It depicts the trade-off between Precision and Recall across different classification thresholds.

• Elbow Curve: The plot helps identify the optimal number of clusters for k-means algorithm.

Over to you: What more plots will you include here?

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Find the code for my tips here: GitHub.

I like to explore, experiment and write about data science concepts and tools. You can read my articles on Medium. Also, you can connect with me on LinkedIn and Twitter.