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Flashcard 7676021050636

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#abm #agent-based #machine-learning #model #priority #synergistic-integration
Question
With the aid of [...], this emerging area can extend traditional agent-based schemes that hardcode agents’ behavioral rules into an adaptive model.
Answer
ML

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With the aid of ML, this emerging area can extend traditional agent-based schemes that hardcode agents’ behavioral rules into an adaptive model.

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Annotation 7676023409932

#tensorflow #tensorflow-certificate

# Create confusion metrics

from sklearn.metrics import confusion_matrix
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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)

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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 7676025244940

Tags
#has-images #tensorflow #tensorflow-certificate
[unknown IMAGE 7626420784396]
Question

How we can improve model (in the particular stage of the process)?

# 1. Creating model: add more layers, increase numbers of hidden neurons, change [...]

Answer
activation functions

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scheduled repetition interval               last repetition or drill

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How we can improve model (in the particular stage of the process)? # 1. Creating model: add more layers, increase numbers of hidden neurons, change activation functions

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TfC 01 regression
#### How we can improve model # 1. Creating model: add more layers, increase numbers of hidden neurons, change activation functions # 2. Compiling: change optimizer or its parameters (eg. learning rate) # 3. Fitting: more epochs, more data ### How? # from smaller model to larger model Evaluating models Typical workflow: build a model -> fit it -> evaulate -> tweak -> fit > evaluate -> .... Building model: experiment Evaluation model: visualize What