Exam Professional Machine Learning Engineer topic 1 question 3 discussion

ANS: C https://developers.google.com/machine-learning/data-prep/construct/sampling-splitting/imbalanced-data#downsampling-and-upweighting - less than 1% of the readings are positive - none of them converge. Downsampling (in this context) means training on a disproportionately low subset of the majority class examples.

=New Question3= You are going to train a DNN regression model with Keras APJs using this code: model - tf.keras.Sequential() model.add(tf.keras.layers.Oense( 256, use_bias-True, activation-•relu', kernel_initializer-None, kernel_regularizer-None, input_shape-(500,))) model.add(tf.keras.layers.Oropout(rate-0.25)) model.add(tf.keras.layers.Oense( 128, use_bias-True, activation-•relu', kernel_initializer-'uniform', kernel_regularizer-'12')) model.add(tf.keras.layers.Oropout(rate-0.25)) model.add(tf.keras.layers.Oense( 2, use_bias-False, activation-•softriax')) model.cornpile(loss-•mse') How many trainable weights does your model have? (The arithmetic below is correct.) A. 501*256+257*128+2 = 161154 B. 500*256+256*128+128*2 = 161024 C. 501*256+257*128+128*2 = 161408 D. 500*256*0(?)25+256*128*0(?)25+128*2 = 4044

The answer is C. We have to note that (1) Downsampling on major class (2) Upsampling on minor class (3) Upweighting on minor class all work for imbalanced data. However, the key assumption in the question is that "You have tried to train several classification models, but none of them converge". You are not asked to tackle imbalanced data but asked to handle the non-convergence problem (due to the limited resources or the poorness of the algorithm). In the official document, it says: "If you have an imbalanced data set, first try training on the true distribution. If the model works well and generalizes, you're done! If not, try the following downsampling and upweighting technique." https://developers.google.com/machine-learning/data-prep/construct/sampling-splitting/imbalanced-data#downsampling-and-upweighting In other words, the "downsampling and upweighting technique" is the technique for the non-convergence problem (not for for the imbalanced data).

C. Downsample the data with upweighting to create a sample with 10% positive examples. Dealing with class imbalance can be challenging for machine learning models. One common approach to resolving the problem is to downsample the data, either by removing examples from the majority class or by oversampling the minority class. In this case, since you have very few positive examples, you would want to oversample the positive examples to create a sample that better represents the underlying distribution of the data. This could involve using upweighting, where positive examples are given a higher weight in the loss function to compensate for their relative scarcity in the data. This can help the model to better focus on the positive examples and improve its performance in classifying failure incidents.

This approach involves downsampling the majority class (negative examples) and upweighting the minority class (positive examples) to create a balanced dataset. By doing so, the model can learn from both classes effectively. Reference: How to Handle Imbalanced Classes in Machine Learning [https://elitedatascience.com/imbalanced-classes]

C - Downsample the majority and add weights to it.

Max Pooling is a pooling operation that calculates the maximum value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer.

Went with C

https://developers.google.com/machine-learning/data-prep/construct/sampling-splitting/imbalanced-data#downsampling-and-upweighting

https://developers.google.com/machine-learning/data-prep/construct/sampling-splitting/imbalanced-data

C. Downsample the data with upweighting to create a sample with 10% positive examples. Dealing with class imbalance can be challenging for machine learning models. One common approach to resolving the problem is to downsample the data, either by removing examples from the majority class or by oversampling the minority class. In this case, since you have very few positive examples, you would want to oversample the positive examples to create a sample that better represents the underlying distribution of the data. This could involve using upweighting, where positive examples are given a higher weight in the loss function to compensate for their relative scarcity in the data. This can help the model to better focus on the positive examples and improve its performance in classifying failure incidents.

Answer would obviously be C As the dataset is imbalanced and you need to resolve this issue in order to obtain desired result the best approach will be to downsample the data.

Best practice for imbalanced dataset is to downsample with upweight https://developers.google.com/machine-learning/data-prep/construct/sampling-splitting/imbalanced-data#downsampling-and-upweighting

Correct answer is "C"

C. because regardless of the model you use, you should always try to transform or adapt your dataset so that it is more balanced

https://developers.google.com/machine-learning/data-prep/construct/sampling-splitting/imbalanced-data

https://developers.google.com/machine-learning/data-prep/construct/sampling-splitting/imbalanced-data