We are going to create an object called MyAutoMLClassifier and we’re going to train and test it on the breast cancer dat

I’m a software engineer working on Firetable, an open-source React app that combines a spreadsheet UI with the full power of Firestore and Firebase. One of its key features is the side drawer, a form-like UI to edit a single row that slides over the main table.

AutoML libraries are very useful tools for a Data Scientist and can actually make us save a lot of time. According to the business context we work on, we may need to work only with certain models or cleaning procedures, so we need our customized version of AutoML.

We have now a complete optimization grid, so we can finally apply the random search to find the best pipeline parameters and save the results in some properties of our object. The random search will apply a 5-fold cross-validation with the scoring function and the number of iterations we have chosen in the class constructor.

Finally, we have to define the ML pipeline, that is built by the pre-processing phase, the feature selection and the model itself. We can set the model to LogisticRegression at the moment; it will be changed later by random search.

As we can see, we are creating an object that will change the scaling among RobustScaler, StandardScaler and MinMaxscaler. Then will change the cleaning strategy among mean and median and will select the features from 1 to the total number of features with steps of 5. Finally, the model itself is set. The random search will check random combinations of this grid, searching for the one that maximizes the performance metrics in cross-validation.

“useRef() is useful for more than the ref attribute. It’s handy for keeping any mutable value around similar to how you’d use instance fields in classes.” — React’s documentation

AutoML libraries are very useful tools for a Data Scientist and can actually make us save a lot of time. According to the business context we work on, we may need to work only with certain models or cleaning procedures, so we need our customized version of AutoML.

Now we have to define a pre-processing pipeline for the categorical variables. This pipeline will clean the blanks using the most frequent value and will one-hot encode them.

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ordering more food online and thus have been increasingly annoyed with mobile experiences in browser. Order Now buttons commonly have to be tapped on twice and some of them aren’t even floating at the bottom of the screen. Again, this may be tricky from a technical perspective, but companies should be aware that (now more than ever) some users will not have your app downloaded.

At the same time, we are going to define a pipeline for the numerical variables, that will be cleaned according to a parameter that will be defined later and scaled according to a scaler that we’ll decide in the random search part.

Refs are a seldom-used feature in React. If you’ve read the official React guide, they’re introduced as an “escape hatch” out of the typical React data flow with a warning to use them sparingly. They’re primarily billed as the correct way to access a component’s underlying DOM element.

As you can see, we are using a Gradient Boosting Tree Classifier with all the features and a numerical cleaning strategy based on the median value. The learning rate of the model is 0.1 and the number of estimators is 125. This is the result of AutoML.

So, we are searching for the best combination of cleaning strategy, scaling procedure, set of features, model and hyperparameters’ values, everything in the same search procedure. This is the core of any AutoML library and can be extended as you want.

Now, we can start defining the MyAutoMLClassifier class. Its constructor will accept a scoring function that will be used in k-fold CV and the number of iterations of the random search. For this example, their default values will be “balanced accuracy” and 50.

Then, we can calculate the number of the features (we’ll need it for the feature selection part) and create an empty list that will contain the optimization grid according to the syntax needed by RandomSearchCV.