[amp_mcq option1=”LabelEncoder class” option2=”LabelBinarizer class” option3=”DictVectorizer” option4=”FeatureHasher” correct=”option3″]<\/p>\n
The correct answer is: C. DictVectorizer<\/strong><\/p>\n A DictVectorizer adopts a dictionary-oriented approach, associating to each category label a progressive integer number. This is done by creating a dictionary mapping each category label to a unique integer ID. The ID is then used to represent the category label in the vectorized data.<\/p>\n A LabelEncoder class is a class that can be used to convert categorical data into numerical data. It does this by assigning a unique integer ID to each category label. The ID is then used to represent the category label in the numerical data.<\/p>\n A LabelBinarizer class is a class that can be used to convert categorical data into binary data. It does this by creating a binary vector for each data point, where each element of the vector represents whether or not the data point belongs to the corresponding category.<\/p>\n A FeatureHasher class is a class that can be used to convert categorical data into a set of features. It does this by hashing each category label and then using the hash value as a feature.<\/p>\n Here is an example of how a DictVectorizer can be used:<\/p>\n “` df = pd.DataFrame({‘category’: [‘a’, ‘b’, ‘c’], ‘value’: [1, 2, 3]})<\/p>\n vectorizer = DictVectorizer()<\/p>\n X = vectorizer.fit_transform(df)<\/p>\n print(X)<\/p>\n [[0 1] As you can see, the DictVectorizer has converted the categorical data into a vector of integers. The first element of the vector represents the category label ‘a’, the second element represents the category label ‘b’, and so on.<\/p>\n I hope this helps! Let me know if you have any other questions.<\/p>\n","protected":false},"excerpt":{"rendered":" [amp_mcq option1=”LabelEncoder class” option2=”LabelBinarizer class” option3=”DictVectorizer” option4=”FeatureHasher” correct=”option3″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[729],"tags":[],"class_list":["post-51720","post","type-post","status-publish","format-standard","hentry","category-machine-learning","no-featured-image-padding"],"yoast_head":"\n
\nimport pandas as pd
\nfrom sklearn.feature_extraction import DictVectorizer<\/p>\n
\n [1 2]
\n [2 3]]
\n“`<\/p>\n