one-hot-encoding

问题 I need to transform the independent field from string to arithmetical notation. I am using OneHotEncoder for the transformation. My dataset has many independent columns of which some are as: Country | Age -------------------------- Germany | 23 Spain | 25 Germany | 24 Italy | 30 I have to encode the Country column like 0 | 1 | 2 | 3 -------------------------------------- 1 | 0 | 0 | 23 0 | 1 | 0 | 25 1 | 0 | 0 | 24 0 | 0 | 1 | 30 I succeed to get the desire transformation via using

问题 Say I have a few features/columns in a dataframe on which I apply the regular OneHotEncoder, and one (let, n-th) column on which I need to apply my custom OneHotEncoder. Then I need to use VectorAssembler to assemble those features, and put into a Pipeline, finally fitting my trainData and getting predictions from my testData, such as: val sIndexer1 = new StringIndexer().setInputCol("my_feature1").setOutputCol("indexed_feature1") // ... let, n-1 such sIndexers for n-1 features val

问题 I have a database consisting of three tables like this: I want to make a machine learning model in R using that database, and the data I need is like this: I can use one hot encoding to convert categorical variable from t_pengolahan (such as "Pengupasan, Fermentasi, etc") into attributes. But, how to set flag (yes or no) to the data value based on "result (using SQL query)" data above? 回答1: We can combine two answers to previous related questions, each of which provides half of the solution;

问题 I just started learning machine learning, when practicing one of the task, I am getting value error, but I followed the same steps as the instructor does. I am getting value error, please help. dff Country Name 0 AUS Sri 1 USA Vignesh 2 IND Pechi 3 USA Raj First I performed labelencoding, X=dff.values label_encoder=LabelEncoder() X[:,0]=label_encoder.fit_transform(X[:,0]) out: X array([[0, 'Sri'], [2, 'Vignesh'], [1, 'Pechi'], [2, 'Raj']], dtype=object) then performed One hot encoding for the

问题 I just started learning machine learning, when practicing one of the task, I am getting value error, but I followed the same steps as the instructor does. I am getting value error, please help. dff Country Name 0 AUS Sri 1 USA Vignesh 2 IND Pechi 3 USA Raj First I performed labelencoding, X=dff.values label_encoder=LabelEncoder() X[:,0]=label_encoder.fit_transform(X[:,0]) out: X array([[0, 'Sri'], [2, 'Vignesh'], [1, 'Pechi'], [2, 'Raj']], dtype=object) then performed One hot encoding for the

问题 I have 3 sets of data (training, validation and testing) and when I run: training_x = pd.get_dummies(training_x, columns=['a', 'b', 'c']) It gives me a certain number of features. But then when I run it across validation data, it gives me a different number and the same for testing. Is there any way to normalize (wrong word, I know) across all data sets so the number of features aligns? 回答1: dummies should be created before dividing the dataset into train, test or validate suppose i have

问题 I have multi-class classification using RNN and here is my main code for RNN: def RNN(x, weights, biases): x = tf.unstack(x, input_size, 1) lstm_cell = rnn.BasicLSTMCell(num_unit, forget_bias=1.0, state_is_tuple=True) stacked_lstm = rnn.MultiRNNCell([lstm_cell]*lstm_size, state_is_tuple=True) outputs, states = tf.nn.static_rnn(stacked_lstm, x, dtype=tf.float32) return tf.matmul(outputs[-1], weights) + biases logits = RNN(X, weights, biases) prediction = tf.nn.softmax(logits) cost =tf.reduce

问题 This question already has answers here : Generate a dummy-variable (16 answers) Closed 2 years ago . I have a medium large dataframe, for which I want to transform one column with categories to binary columns, one for each category. At the same time, I want to keep the rest of the columns in the dataframe. What would be the easiest way to achieve this? Here is an example of what I want to do: d<-data.frame(ID=c("a","b","c","d"), Gender=c("male", "male", "female","female"), Age =c(23,45,18,11)

问题 I have a dataframe looks like this: JOINED_CO GENDER EXEC_FULLNAME GVKEY YEAR CONAME BECAMECEO REJOIN LEFTOFC LEFTCO RELEFT REASON PAGE CO_PER_ROL 5622 NaN MALE Ira A. Eichner 1004 1992 AAR CORP 19550101 NaN 19961001 19990531 NaN RESIGNED 79 5622 NaN MALE Ira A. Eichner 1004 1993 AAR CORP 19550101 NaN 19961001 19990531 NaN RESIGNED 79 5622 NaN MALE Ira A. Eichner 1004 1994 AAR CORP 19550101 NaN 19961001 19990531 NaN RESIGNED 79 5622 NaN MALE Ira A. Eichner 1004 1995 AAR CORP 19550101 NaN

问题 I have a dataframe looks like this: JOINED_CO GENDER EXEC_FULLNAME GVKEY YEAR CONAME BECAMECEO REJOIN LEFTOFC LEFTCO RELEFT REASON PAGE CO_PER_ROL 5622 NaN MALE Ira A. Eichner 1004 1992 AAR CORP 19550101 NaN 19961001 19990531 NaN RESIGNED 79 5622 NaN MALE Ira A. Eichner 1004 1993 AAR CORP 19550101 NaN 19961001 19990531 NaN RESIGNED 79 5622 NaN MALE Ira A. Eichner 1004 1994 AAR CORP 19550101 NaN 19961001 19990531 NaN RESIGNED 79 5622 NaN MALE Ira A. Eichner 1004 1995 AAR CORP 19550101 NaN