The valid method for handling categorical data in Machine Learning from the given options is A) One-hot encoding.

Here's why:

A) One-hot encoding: This is a process of converting categorical data variables so they can be provided to machine learning algorithms to improve predictions. With one-hot, we convert each categorical value into a new categorical column and assign a binary value of 1 or 0. Each integer value is represented as a binary vector. This is the correct method.

B) Mean normalization: This is a method used to standardize the range of independent variables or features of data. It can make the mean of the attribute equal to zero, but it doesn't help in converting categorical data.

C) Log transformation: This is a data transformation method in which it replaces each variable x with a log(x). The choice of the logarithm base is usually left up to the analyst and it would depend on the purposes of statistical modeling. But again, it doesn't help in converting categorical data.

D) Principal Component Analysis: This is a technique used for identification of a smaller number of uncorrelated variables known as principal components from a larger set of data. The technique is widely used for applications of dimensionality reduction, but not for handling categorical data.

Question

The valid method for handling categorical data in Machine Learning from the given options is A) One-hot encoding.

Here's why:

A) One-hot encoding: This is a process of converting categorical data variables so they can be provided to machine learning algorithms to improve predictions. With one-hot, we convert each categorical value into a new categorical column and assign a binary value of 1 or 0. Each integer value is represented as a binary vector. This is the correct method.

B) Mean normalization: This is a method used to standardize the range of independent variables or features of data. It can make the mean of the attribute equal to zero, but it doesn't help in converting categorical data.

C) Log transformation: This is a data transformation method in which it replaces each variable x with a log(x). The choice of the logarithm base is usually left up to the analyst and it would depend on the purposes of statistical modeling. But again, it doesn't help in converting categorical data.

D) Principal Component Analysis: This is a technique used for identification of a smaller number of uncorrelated variables known as principal components from a larger set of data. The technique is widely used for applications of dimensionality reduction, but not for handling categorical data.

Knowee AI · Accepted Answer

The valid method for handling categorical data in Machine Learning from the given options is A) One-hot encoding.

Here's why:

A) One-hot encoding: This is a process of converting categorical data variables so they can be provided to machine learning algorithms to improve predictions. With one-hot, we convert each categorical value into a new categorical column and assign a binary value of 1 or 0. Each integer value is represented as a binary vector. This is the correct method.

B) Mean normalization: This is a method used to standardize the range of independent variables or features of data. It can make the mean of the attribute equal to zero, but it doesn't help in converting categorical data.

C) Log transformation: This is a data transformation method in which it replaces each variable x with a log(x). The choice of the logarithm base is usually left up to the analyst and it would depend on the purposes of statistical modeling. But again, it doesn't help in converting categorical data.

D) Principal Component Analysis: This is a technique used for identification of a smaller number of uncorrelated variables known as principal components from a larger set of data. The technique is widely used for applications of dimensionality reduction, but not for handling categorical data.

Which of the following is a valid method for handling categorical data in ML?*1 pointo A) One-hot encodingo B) Mean normalizationo C) Log transformationo D) Principal Component Analysis

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