דסבת

In a dataset containing a million samples where only a thousand lack data, what strategy can be employed to handle the missing records?

1

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What approach can be taken to handle missing values in a dataset with limited data?

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What is the first step in the feature engineering process?

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When is normalization typically applied in feature engineering?

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What is the purpose of feature selection in feature engineering?

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What is the correct sequence of steps in the feature engineering process?

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Which encoding technique expands the categorical feature into multiple binary columns, each representing a unique category?

1

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In One-Hot Encoding, how many columns are created for a categorical feature with n unique categories?

1

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Label Encoding involves assigning a unique numerical label to each category in a categorical feature. For example, converting categorical labels like "low," "medium," and "high" to numerical values 0, 1, and 2.

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One-Hot Encoding converts each categorical value into a new binary column and assigns a 1 or 0 to indicate the presence or absence of that category. For example, converting categorical values like "red," "blue," and "green" into three separate binary columns.

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Discuss, Learn and be Happy דיון בשאלות

In a dataset containing a million samples where only a thousand lack data, what strategy can be employed to handle the missing records?

What approach can be taken to handle missing values in a dataset with limited data?

What is the first step in the feature engineering process?

When is normalization typically applied in feature engineering?

What is the purpose of feature selection in feature engineering?

What is the correct sequence of steps in the feature engineering process?

Which encoding technique expands the categorical feature into multiple binary columns, each representing a unique category?

In One-Hot Encoding, how many columns are created for a categorical feature with n unique categories?

Label Encoding involves assigning a unique numerical label to each category in a categorical feature. For example, converting categorical labels like "low," "medium," and "high" to numerical values 0, 1, and 2.

One-Hot Encoding converts each categorical value into a new binary column and assigns a 1 or 0 to indicate the presence or absence of that category. For example, converting categorical values like "red," "blue," and "green" into three separate binary columns.