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on 28-May-2024 (Tue)

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Flashcard 7628134944012

Tags
#tensorflow #tensorflow-certificate
Question
Recall

[...] recall leads to less false negatives.

Answer
Higher

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Parent (intermediate) annotation

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Recall Higher recall leads to less false negatives.

Original toplevel document

TfC_02_classification-PART_2
ccuracy tf.keras.metrics.Accuracy() sklearn.metrics.accuracy_score() Not the best for imbalanced classes Precision For imbalanced class problems. Higher precision leads to less false positives. <span>Recall Higher recall 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 Confusion matrix Can b






Annotation 7628137041164

#tensorflow #tensorflow-certificate

Bag of tricks to improve model

  1. Create model - more layers, more neurons, different activation
  2. Compile mode - other loss, other optimizer, change optimizer parameters
  3. Fit the model - more epochs, more data examples
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TfC_02_classification-PART_1
nse(10, activation='relu'), tf.keras.layers.Dense(1, activation='sigmoid') ]) model.compile(optimizer=tf.keras.optimizers.Adam(), loss=tf.keras.losses.binary_crossentropy, metrics=['accuracy']) <span>Bag of tricks to improve model Create model - more layers, more neurons, different activation Compile mode - other loss, other optimizer, change optimizer parameters Fit the model - more epochs, more data examples # plots model predictions agains true data import numpy as np def plot_decision_boundry(model, X, y): """ Take in a trained model, features and labels and create numpy.meshgrid of the d