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on 14-Jul-2025 (Mon)

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Flashcard 7715047476492

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#tensorflow #tensorflow-certificate
Question

# Create confusion matrix

from sklearn.[...] import confusion_matrix

Answer
metrics

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

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# Create confusion metrics from sklearn.metrics import confusion_matrix

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TfC_02_classification-PART_2
leads to less false negatives. Tradeoff between recall and precision. F1-score Combination of precision and recall, ususally a good overall metric for classification models. keyboard_arrow_down <span>Confusion matrix Can be hard to use whith large numbers of classes. y-axis -> true label x-axis -> predicted label # Create confusion metrics from sklearn.metrics import confusion_matrix y_preds = model_8.predict(X_test) confusion_matrix(y_test, y_preds) important: This time there is a problem with loss function. In case of categorical_crossentropy the labels have to be one-hot encoded In case of labels as integeres use SparseCategorica






Flashcard 7715050360076

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#has-images #tensorflow #tensorflow-certificate
[unknown IMAGE 7626420784396]
Question
Huber
  • tf.keras.losses.Huber()
  • combintion of MSE and [...] less sensitive to outliers than MSE
Answer
MAE

statusnot learnedmeasured difficulty37% [default]last interval [days]               
repetition number in this series0memorised on               scheduled repetition               
scheduled repetition interval               last repetition or drill

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Huber tf.keras.losses.Huber() combintion of MSE and MAE less sensitive to outliers than MSE

Original toplevel document

TfC 01 regression
st_labels, c="green", label="Testing data") plt.scatter(test_data, predictions, c="red", label="Predictions") plt.legend(); Common regression evaluation metrics keyboard_arrow_down Introduction <span>For regression problems: MAE tf.keras.losses.MAE() tf.metrics.mean_absolute_error() great starter metrics for any regression problem MSE tf.keras.losses.MSE() tf.metrics.mean_square_error() when larger errors are more significant that smaller errors Huber tf.keras.losses.Huber() combintion of MSE and MAE less sensitive to outliers than MSE Take away: You should minimize the time between your experiments (that's way you should start with smaller models). The more experiments you do, the more things you figure out that don't work. Tracking your experiments One really good habit is to track the results of your experiments. There are tools to help us! Resource: Try: Tensorboard - a component of Tensorflow library t