Tag Archives: MLS-C01 Exam Dumps

[November-2020]MLS-C01 VCE MLS-C01 123Q Dumps Free Offered by Braindump2go[Q82-Q102]

November/2020 Latest Braindump2go MLS-C01 Exam Dumps with PDF and VCE Free Updated Today! Following are some new MLS-C01 Real Exam Questions!

QUESTION 82
A Data Scientist is building a model to predict customer churn using a dataset of 100 continuous numerical features. The Marketing team has not provided any insight about which features are relevant for churn prediction. The Marketing team wants to interpret the model and see the direct impact of relevant features on the model outcome. While training a logistic regression model, the Data Scientist observes that there is a wide gap between the training and validation set accuracy.
Which methods can the Data Scientist use to improve the model performance and satisfy the Marketing team’s needs? (Choose two.)

A. Add L1 regularization to the classifier
B. Add features to the dataset
C. Perform recursive feature elimination
D. Perform t-distributed stochastic neighbor embedding (t-SNE)
E. Perform linear discriminant analysis

Answer: BE

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[2020-April-New]Real Exam Questions-Braindump2go MLS-C01 Dumps 81Q Download

2020/April New Braindump2go MLS-C01 Exam Dumps with PDF and VCE Free Updated Today! Following are some new MLS-C01 Exam Questions!

New Question
A Machine Learning Specialist built an image classification deep learning model. However, the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%, respectively.
How should the Specialist address this issue and what is the reason behind it?

A. The learning rate should be increased because the optimization process was trapped at a local minimum.
B. The dropout rate at the flatten layer should be increased because the model is not generalized enough.
C. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
D. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.

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