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Which of the following cross validation versions may not be suitable for very large datasets with hundreds of thousands of samples?
Leave-one-out cross-validation (LOO cross-validation) is not suitable for very large datasets due to the fact that this validation technique requires one model for every sample in the training set to be created and evaluated.
Cross validation
It is a technique to evaluate a machine learning model and it is the basis for whole class of model evaluation methods. The goal of cross-validation is to test the model's ability to predict new data that was not used in estimating it. It works by the idea of splitting dataset into number of subsets, keep a subset aside, train the model, and test the model on the holdout subset.
Leave-one-out cross validation
Leave-one-out cross validation is K-fold cross validation taken to its logical extreme, with K equal to N, the number of data points in the set. That means that N separate times, the function approximator is trained on all the data except for one point and a prediction is made for that point. As be-fore the average error is computed and used to evaluate the model. The evaluation given by leave-one-out cross validation is very expensive to compute at first pass.
You previously trained a model using a training dataset. You want to detect any data drift in the new data collected since the model was trained.
What should you do?
To track changing data trends, create a data drift monitor that uses the training data as a baseline and the new data as a target.
Model drift and decay are concepts that describe the process during which the performance of a model deployed to production degrades on new, unseen data or the underlying assumptions about the data change.
These are important metrics to track once models are deployed to production. Models must be regularly re-trained on new data. This is referred to as refitting the model. This can be done either on a periodic basis, or, in an ideal scenario, retraining can be triggered when the performance of the model degrades below a certain pre-defined threshold.
Consider a data frame df with 10 rows and index [ 'r1', 'r2', 'r3', 'row4', 'row5', 'row6', 'r7', 'r8', 'r9', 'row10']. What does the expression g = df.groupby(df.index.str.len()) do?
Data frames cannot be grouped by index values. Hence it results in Error.
What is the formula for measuring skewness in a dataset?
Since the normal curve is symmetric about its mean, its skewness is zero. This is a theoretical expla-nation for mathematical proofs, you can refer to books or websites that speak on the same in detail.
What Can Snowflake Data Scientist do in the Snowflake Marketplace as Consumer?
As a consumer, you can do the following:
* Discover and test third-party data sources.
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* Combine new datasets with your existing data in Snowflake to derive new business insights.
* Have datasets available instantly and updated continually for users.
* Eliminate the costs of building and maintaining various APIs and data pipelines to load and up-date data.
* Use the business intelligence (BI) tools of your choice.
