So, I have this doubt and have been looking for answers. So the question is when I use,
from sklearn import preprocessing
min_max_scaler = preprocessing.MinMaxScaler()
df = pd.DataFrame({'A':[1,2,3,7,9,15,16,1,5,6,2,4,8,9],'B':[15,12,10,11,8,14,17,20,4,12,4,5,17,19],'C':['Y','Y','Y','Y','N','N','N','Y','N','Y','N','N','Y','Y']})
df[['A','B']] = min_max_scaler.fit_transform(df[['A','B']])
df['C'] = df['C'].apply(lambda x: 0 if x.strip()=='N' else 1)
After which I will train and test the model (A
,B
as features, C
as Label) and get some accuracy score. Now my doubt is, what happens when I have to predict the label for new set of data. Say,
df = pd.DataFrame({'A':[25,67,24,76,23],'B':[2,54,22,75,19]})
Because when I normalize the column the values of A
and B
will be changed according to the new data, not the data which the model will be trained on.
So, now my data after the data preparation step that is as below, will be.
data[['A','B']] = min_max_scaler.fit_transform(data[['A','B']])
Values of A
and B
will change with respect to the Max
and Min
value of df[['A','B']]
. The data prep of df[['A','B']]
is with respect to Min Max
of df[['A','B']]
.
How can the data preparation be valid with respect to different numbers relate? I don't understand how the prediction will be correct here.
MinMaxScaler
using the training
data and then apply the scaler on the testing
data before the prediction.In summary:
scaler
on the TRAINING data
scaler
to transform the TRAINING data
transformed training data
to fit the predictive model
scaler
to transform the TEST data
predict
using the trained model
(step 3) and the transformed TEST data
(step 4).Example using your data:
from sklearn import preprocessing
min_max_scaler = preprocessing.MinMaxScaler()
#training data
df = pd.DataFrame({'A':[1,2,3,7,9,15,16,1,5,6,2,4,8,9],'B':[15,12,10,11,8,14,17,20,4,12,4,5,17,19],'C':['Y','Y','Y','Y','N','N','N','Y','N','Y','N','N','Y','Y']})
#fit and transform the training data and use them for the model training
df[['A','B']] = min_max_scaler.fit_transform(df[['A','B']])
df['C'] = df['C'].apply(lambda x: 0 if x.strip()=='N' else 1)
#fit the model
model.fit(df['A','B'])
#after the model training on the transformed training data define the testing data df_test
df_test = pd.DataFrame({'A':[25,67,24,76,23],'B':[2,54,22,75,19]})
#before the prediction of the test data, ONLY APPLY the scaler on them
df_test[['A','B']] = min_max_scaler.transform(df_test[['A','B']])
#test the model
y_predicted_from_model = model.predict(df_test['A','B'])
Example using iris data:
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.svm import SVC
data = datasets.load_iris()
X = data.data
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)
scaler = MinMaxScaler()
X_train_scaled = scaler.fit_transform(X_train)
model = SVC()
model.fit(X_train_scaled, y_train)
X_test_scaled = scaler.transform(X_test)
y_pred = model.predict(X_test_scaled)
Hope this helps.
See also by post here: https://towardsdatascience.com/everything-you-need-to-know-about-min-max-normalization-in-python-b79592732b79