CS-6309 - Introduction to Machine Learning oed answer key
Showing 141 to 160 of 163 total answers.
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Awesome StudentQuestion • Introduction to Machine Learning
You can easily visualize the 3D prediction of function since you are aware of the intercept and coefficients of the polynomial multiple regression function
Answer
False
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Awesome StudentQuestion • Introduction to Machine Learning
It is not possible to draw a straight line to separate two sets of points However, you can make this set of points linearly separable with some manipulation
Answer
True
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Awesome StudentQuestion • Introduction to Machine Learning
Matplotlib makes it simple to create sophisticated charts and figures, and its combination with Jupyter Notebook makes it an excellent machine learning tool
Answer
True
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Awesome StudentQuestion • Introduction to Machine Learning
Using a linear object, you can build a new feature matrix from any polynomial combinations of features with a degree less than or equal to the desired degree
Answer
False
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Awesome StudentQuestion • Introduction to Machine Learning
Since you are familiar with the intercept and coefficients of the polynomial multiple regression function, you can quickly visualize the 3D prediction function
Answer
False
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Awesome StudentQuestion • Introduction to Machine Learning
Use DataFrameplot to access the shape of a Pandas DataFrame; the shape property provides a tuple that indicates the dimensionality of the DataFrame DataFrameshape
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Awesome StudentQuestion • Introduction to Machine Learning
A model that detects fraudulent credit card use, for example, would be trained using a dataset that included labeled data points of known fraudulent and valid charges
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Awesome StudentQuestion • Introduction to Machine Learning
It's worth noting that the sort_values() function only accepts Series objects with indexes equal to the DataFrame's index (axis=0) or the DataFrame's columns (axis=1)
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Awesome StudentQuestion • Introduction to Machine Learning
If the random state parameter of the train_test_curve() function is not supplied, you will receive a different training and testing set each time you call this function
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Awesome StudentQuestion • Introduction to Machine Learning
You can create a new feature matrix made up of all polynomial combinations of the features with a degree less than or equal to the specified degree by using a linear object
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Awesome StudentQuestion • Introduction to Machine Learning
Although printing the predictions alongside the test set's original diagnoses is useful, it does not give a clear sense of how well the model predicts if a tumor is malignant
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Awesome StudentQuestion • Introduction to Machine Learning
To run or execute a code in Jupyter Notebook, you need to save the code with an extension name of py and remove the first line that contains the expression "%matploblib inline"
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Awesome StudentQuestion • Introduction to Machine Learning
The make regression() function produces linearly distributed data wherein you can define the number of features and the standard deviation of the Gaussian noise used in an output
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Awesome StudentQuestion • Introduction to Machine Learning
Because the data distribution in datasets might not be uniform, your test set might be difficult to anticipate, making it impossible to determine whether your model is inefficient
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Awesome StudentQuestion • Introduction to Machine Learning
Loading the dataset into a Pandas dataframe and then using the predict() function to check for null values in the dataframe is an effective technique to detect empty rows Isnull()
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Awesome StudentQuestion • Introduction to Machine Learning
Rather than having to retrain the model each time you have new data to test, a stored model allows you to load the trained model and make predictions without having to retrain the model
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Awesome StudentQuestion • Introduction to Machine Learning
The next step is to identify the best K that yields the highest average Silhouette Coefficient once you have seen how to calculate the mean Silhouette Coefficient for a dataset with K clusters
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Awesome StudentQuestion • Introduction to Machine Learning
Anaconda is an open source package management system and environment management system for installing multiple versions of software packages and their dependencies and switching easily between them
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Awesome StudentQuestion • Introduction to Machine Learning