如果不是，可以使用scikit-learn而不是二进制分类来预测变量

Question

I am working in the field of Pharmaceutical sciences, I work on chemical compounds and with calculating their chemical properties or descriptors we can predict certain biological function of that compounds. I use python and R programming language for the same and also use Weka machine learning tool. Weka provides facility for binary prediction using SVM and other supporting algorithms.

Ex data set: Training set

Chem_ID   MW LogP HbD HbE IC50 Class_label
  001    232  5    0   2    20    0
  002    280  2    1   4    41    1
  003    240  5    0   2    22    0
  004    300  4    1   5    48    1
  005    245  2    0   2    24    0
  006    255  1    0   2    20    0
  007    299  5    1   4    49    1

Test set

Chem_ID  MW   LogP HbD HbE IC50 Class_label
    000   255  1    0   2    20    

In weka there are few algorithm with them we can predict the "class_label" or we can also predict specific variable (we usually predict "IC50" values ), does scikit-learn or any other machine learning library in python having that capabilities. if yes how can we use it thanks.

Answer 1

Yes, this is a regression problem. There are many different models to solve a regression problem, from a simple Linear Regression , to Support Vector Regression or Decision Tree Regressors (and many more).

They work similarly to binary classifier: You give them your training data and instead of 0/1 labels you give them target values to train. In your case you would take the feature you want to predict as target value and delete it form the training data.

Short example:

target_values = training_set['IC50']
training_data = training_set.drop('IC50')

clf = LinearRegression()
clf.fit(training_data, target_values)

test_data = test_set.drop('IC50')

predicted_values = clf.predict(test_data)