one-hot-encoding | 易学教程

Can sklearn random forest directly handle categorical features?

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问题 Say I have a categorical feature, color, which takes the values ['red', 'blue', 'green', 'orange'], and I want to use it to predict something in a random forest. If I one-hot encode it (i.e. I change it to four dummy variables), how do I tell sklearn that the four dummy variables are really one variable? Specifically, when sklearn is randomly selecting features to use at different nodes, it should either include the red, blue, green and orange dummies together, or it shouldn't include any of

One Hot Encoding using numpy [duplicate]

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问题 This question already has answers here : Convert array of indices to 1-hot encoded numpy array (17 answers) Closed 9 months ago . If the input is zero I want to make an array which looks like this: [1,0,0,0,0,0,0,0,0,0] and if the input is 5: [0,0,0,0,0,1,0,0,0,0] For the above I wrote: np.put(np.zeros(10),5,1) but it did not work. Is there any way in which, this can be implemented in one line? 回答1: Usually, when you want to get a one-hot encoding for classification in machine learning, you

One hot encoding of string categorical features

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问题 I'm trying to perform a one hot encoding of a trivial dataset. data = [['a', 'dog', 'red'] ['b', 'cat', 'green']] What's the best way to preprocess this data using Scikit-Learn? On first instinct, you'd look towards Scikit-Learn's OneHotEncoder. But the one hot encoder doesn't support strings as features; it only discretizes integers. So then you would use a LabelEncoder, which would encode the strings into integers. But then you have to apply the label encoder into each of the columns and

How to one hot encode several categorical variables in R

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问题 I'm working on a prediction problem and I'm building a decision tree in R, I have several categorical variables and I'd like to one-hot encode them consistently in my training and testing set. I managed to do it on my training data with : temps <- X_train tt <- subset(temps, select = -output) oh <- data.frame(model.matrix(~ . -1, tt), CLASS = temps$output) But I can't find a way to apply the same encoding on my testing set, how can I do that? 回答1: I recommend using the dummyVars function in

How can I one hot encode in Python?

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I have a machine learning classification problem with 80% categorical variables. Must I use one hot encoding if I want to use some classifier for the classification? Can i pass the data to a classifier without the encoding? I am trying to do the following for feature selection: I read the train file: num_rows_to_read = 10000 train_small = pd.read_csv("../../dataset/train.csv", nrows=num_rows_to_read) I change the type of the categorical features to 'category': non_categorial_features = ['orig_destination_distance', 'srch_adults_cnt', 'srch_children_cnt', 'srch_rm_cnt', 'cnt'] for categorical

Scikit Learn OneHotEncoder fit and transform Error: ValueError: X has different shape than during fitting

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问题 Below is my code. I know why the error is occurring during transform. It is because of the feature list mismatch during fit and transform. How can i solve this? How can i get 0 for all the rest features? After this i want to use this for partial fit of SGD classifier. Jupyter QtConsole 4.3.1 Python 3.6.2 |Anaconda custom (64-bit)| (default, Sep 21 2017, 18:29:43) Type \'copyright\', \'credits\' or \'license\' for more information IPython 6.1.0 -- An enhanced Interactive Python. Type \'?\' for

Convert array of indices to 1-hot encoded numpy array

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Let's say I have a 1d numpy array a = array([1,0,3]) I would like to encode this as a 2d 1-hot array b = array([[0,1,0,0], [1,0,0,0], [0,0,0,1]]) Is there a quick way to do this? Quicker than just looping over a to set elements of b , that is. Your array a defines the columns of the nonzero elements in the output array. You need to also define the rows and then use fancy indexing: >>> a = np.array([1, 0, 3]) >>> b = np.zeros((3, 4)) >>> b[np.arange(3), a] = 1 >>> b array([[ 0., 1., 0., 0.], [ 1., 0., 0., 0.], [ 0., 0., 0., 1.]]) >>> values = [1, 0, 3] >>> n_values = np.max(values) + 1 >>> np

How can I one hot encode in Python?

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问题 I have a machine learning classification problem with 80% categorical variables. Must I use one hot encoding if I want to use some classifier for the classification? Can i pass the data to a classifier without the encoding? I am trying to do the following for feature selection: I read the train file: num_rows_to_read = 10000 train_small = pd.read_csv(\"../../dataset/train.csv\", nrows=num_rows_to_read) I change the type of the categorical features to \'category\': non_categorial_features = [\

Convert array of indices to 1-hot encoded numpy array

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问题 Let\'s say I have a 1d numpy array a = array([1,0,3]) I would like to encode this as a 2d 1-hot array b = array([[0,1,0,0], [1,0,0,0], [0,0,0,1]]) Is there a quick way to do this? Quicker than just looping over a to set elements of b , that is. 回答1: Your array a defines the columns of the nonzero elements in the output array. You need to also define the rows and then use fancy indexing: >>> a = np.array([1, 0, 3]) >>> b = np.zeros((3, 4)) >>> b[np.arange(3), a] = 1 >>> b array([[ 0., 1., 0.,