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b/docs/posts/classification-metrics-on-the-backend/index.qmd new file mode 100644 index 000000000000..733774e8c97e --- /dev/null +++ b/docs/posts/classification-metrics-on-the-backend/index.qmd @@ -0,0 +1,183 @@ +--- +title: "Classification metrics on the backend" +author: "Tyler White" +date: "2024-11-15" +image: thumbnail.png +categories: + - blog + - machine learning + - portability +--- + +A review of binary classification models, metrics used to evaluate them, and performing +the metric calculations with Ibis. + +We're going explore common classification metrics such as accuracy, precision, recall, +and F1 score, demonstrating how to compute each one using Ibis. In this example, we'll +use DuckDB, the default Ibis backend, but we could use this same code to execute +against another backend such as Postgres or Snowflake. This capability is useful as it +offers an easy and performant way to evaluate model performance without extracting data +from the source system. + +## Classification models + +In machine learning, classification entails categorizing data into different groups. +Binary classification, which is what we'll be covering in this post, specifically +involves sorting data into only two distinct groups. For example, a model could +differentiate between whether or not an email is spam. + +## Model evaluation + +It's important to validate the performance of the model to ensure it makes correct +predictions consistently and doesn’t only perform well on the data it was trained on. +These metrics help us understand not just the errors the model makes, but also the +types of errors. For example, we might want to know if the model is more likely to +predict a positive outcome when the actual outcome is negative. + +The easiest way to breakdown how this works is to look at a confusion matrix. + +### Confusion matrix + +A confusion matrix is a table used to describe the performance of a classification +model on a set of data for which the true values are known. As binary classification +only involves two categories, the confusion matrix is a simple 2x2 table where each +cell shows the count of true positives, false positives, false negatives, and true +negatives. + +![](confusion_matrix.png) + +Here's a breakdown of the terms with examples. + +True Positives (TP) +: Correctly predicted positive examples. + +We guessed it was a spam email, and it was. This email is going straight to the junk +folder. + +False Positives (FP) +: Incorrectly predicted as positive. + +We guessed it was a spam email, but it actually wasn’t. Hopefully, the recipient +doesn’t miss anything important as this email is going to the junk folder. + +False Negatives (FN) +: Incorrectly predicted as negative. + +We didn't guess it was a spam email, but it really was. Hopefully, the recipient +doesn’t click any links! + +True Negatives (TN) +: Correctly predicted negative examples. + +We guessed it was not a spam email, and it actually was not. The recipient can read +this email as intended. + +### Building a confusion matrix + +#### Sample dataset + +Let's create a sample dataset that includes twelve rows with two columns: `actual` and +`prediction`. The `actual` column contains the true values, and the `prediction` column +contains the model's predictions. + +```{python} + +from ibis.interactive import * + +t = ibis.memtable( + { + "id": range(1, 13), + "actual": [1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1], + "prediction": [1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1], + } +) + +t +``` + +We can use the `case` function to create a new column that categorizes the outcomes. + +```{python} + +case_expr = ( + ibis.case() + .when((_.actual == 0) & (_.prediction == 0), "TN") + .when((_.actual == 0) & (_.prediction == 1), "FP") + .when((_.actual == 1) & (_.prediction == 0), "FN") + .when((_.actual == 1) & (_.prediction == 1), "TP") + .end() +) + +t = t.mutate(outcome=case_expr) + +t +``` + +To create the confusion matrix, we'll group by the outcome, count the occurrences, and +use `pivot_wider`. Widening our data makes it possible to perform column-wise +operations on the table expression for metric calculations. + +```{python} + +cm = ( + t.group_by("outcome") + .agg(counted=_.count()) + .pivot_wider(names_from="outcome", values_from="counted") +) + +cm +``` + +Now that we've built a confusion matrix, we're able to more easily calculate a few +common classification metrics. + +### Metrics + +Here are the metrics we'll calculate as well as a brief description of each. + +Accuracy +: The proportion of correct predictions out of all predictions made. This measures the +overall effectiveness of the model across all classes. + +Precision +: The proportion of true positive predictions out of all positive predictions made. +This tells us how many of the predicted positives were actually correct. + +Recall +: The proportion of true positive predictions out of all actual positive examples. This +measures how well the model identifies all actual positives. + +F1 Score +: A metric that combines precision and recall into a single score by taking their +weighted average. This balances the trade-off between precision and recall, making it +especially useful for imbalanced datasets. + +We can calculate these metrics using the columns from the confusion matrix we created +earlier. + +```{python} + +accuracy_expr = (_.TP + _.TN) / (_.TP + _.TN + _.FP + _.FN) +precision_expr = _.TP / (_.TP + _.FP) +recall_expr = _.TP / (_.TP + _.FN) +f1_score_expr = 2 * (precision_expr * recall_expr) / (precision_expr + recall_expr) + +metrics = cm.select( + accuracy=accuracy_expr, + precision=precision_expr, + recall=recall_expr, + f1_score=f1_score_expr, +) + +metrics +``` + +## Conclusion + +By pushing the computation down to the backend, the performance is as powerful as the +backend we're connected to. This capability allows us to easily scale to different +backends without modifying any code. + +We hope you give this a try and let us know how it goes. If you have any questions or +feedback, please reach out to us on [GitHub](https://github.com/ibis-project) or +[Zulip](https://ibis-project.zulipchat.com/). diff --git a/docs/posts/classification-metrics-on-the-backend/thumbnail.png b/docs/posts/classification-metrics-on-the-backend/thumbnail.png new file mode 100644 index 0000000000000000000000000000000000000000..ae2f53296272636b2492e087c72d414153a9b284 GIT binary patch literal 777167 zcmZ^}1yo$mvM)S?L(l*Lf(8Nvhru0!yCt|YgZm8b2>}9wI}9Em1cD3}96}h}LU4Dt zKydl;Kj*%C&v(~*d#_$yT~)uT>fOC|udeQo+L}rPc+c?w004oCvb-(;fadT|!NK~= zx$r3d`M1Kblhu#~0BYi&+*x7%Z8O*?>uLZ1{wx4MXgC0H^OqI60|0pO0swoK0DxEq z06^i6Xx5SVEAWLFs@Q610NDT1H~P_}`W!qdg4fF2=*- z$CE4czq<9egFOF8c=)(^dH!!Oh_A!{1NM*P->`r6 z>)-Aq{xK$Iqzi$%IeYyRmXt6^;$I&Af6V`h@81B!{~O>xng1K0<>>(V`$_)gf$zT@ z{3q`}^#6%dOxwX1;%q4I-~w@l{u7muh>!%&|5ft8k#cU%Zk~GXRyGi+zmEO|`486r z*8Z0tqyML$|B(C_QiA87ar=*P{P!gJ7x(Whk;0SU`R^Ggg_kTO@dE&m0jS8!zVQXZ z7JU+IcfIH?=9>ci{jIeQax-&VYimXAEYmTRGXbxjf)fiFH)YZeJ++ZvKL4gqGxJXU 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