I have a simple linear regression model below that uses one hot encoding to transform every X value. My question is how can I modify the code below to use one hot encoding for every column except one (e.g. the integer one highlighted below)
from numpy import mean
from numpy import std
from pandas import read_csv
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.metrics import accuracy_score
# define the location of the dataset
url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/breast-cancer.csv"
# load the dataset
dataset = read_csv(url, header=None)
# retrieve the array of data
data = dataset.values
# separate into input and output columns
X = data[:, :-1].astype(str)
y = data[:, -1].astype(str)
# split the dataset into train and test sets
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.33, random_state=1)
# one-hot encode input variables that are objects
onehot_encoder = OneHotEncoder()
onehot_encoder.fit(X_train)
X_train = onehot_encoder.transform(X_train)
X_test = onehot_encoder.transform(X_test)
# ordinal encode target variable
label_encoder = LabelEncoder()
label_encoder.fit(y_train)
y_train = label_encoder.transform(y_train)
y_test = label_encoder.transform(y_test)
# define the model
model = LogisticRegression()
# fit on the training set
model.fit(X_train, y_train)
# predict on test set
yhat = model.predict(X_test)
# evaluate predictions
accuracy = accuracy_score(y_test, yhat)
print('Accuracy: %.2f' % (accuracy*100))
I tried only feeding in 8 columns instead of 9 to OHE but got the error:
ValueError: The number of features in X is different to the number of features of the fitted data. The fitted data had 9 features and the X has 8 features.
