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Chapter 14Figure 43: AutoML Vision – evaluating the results: confusion matrixNote that again, the AutoML generated model is comparable or even better thanthe models manually crafted at the end of 2019. Indeed, the best model (https://www.kaggle.com/aakashnain/beating-everything-with-depthwiseconvolution)available at the end of 2019 reached a recall of 0.98 and a precisionof 0.79 (see Figure 44):Figure 44: Chest X-Ray Images – manually crafted models on Kaggle[ 523 ]
An introduction to AutoMLUsing Cloud AutoML ‒ Text ClassificationsolutionIn this section we are going to build a classifier using AutoML. Let's activate thetext classification solution via https://console.cloud.google.com/naturallanguage/(see Figure 45 and 46):Figure 45: AutoML Text Classification – accessing the natural language interfaceFigure 46: AutoML Text Classification – launching the applicationWe are going to use a dataset already available online (https://cloud.google.com/natural-language/automl/docs/sample/happiness.csv), load it into adataset named "happiness," and perform a single-label classification (see Figure 47).The file is uploaded from my computer (see Figure 48):[ 524 ]
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An introduction to AutoML
Using Cloud AutoML ‒ Text Classification
solution
In this section we are going to build a classifier using AutoML. Let's activate the
text classification solution via https://console.cloud.google.com/naturallanguage/
(see Figure 45 and 46):
Figure 45: AutoML Text Classification – accessing the natural language interface
Figure 46: AutoML Text Classification – launching the application
We are going to use a dataset already available online (https://cloud.google.
com/natural-language/automl/docs/sample/happiness.csv), load it into a
dataset named "happiness," and perform a single-label classification (see Figure 47).
The file is uploaded from my computer (see Figure 48):
[ 524 ]