title: Decision tree with a probability target
body: <p>I'm currently working on a model to predict a probability of fatality once a person is infected with the Corona virus.
  I'm using a Dutch dataset with categorical variables: date of infection, fatality or cured, gender, age-group etc.
  It was suggested to use a decision tree, which I've already built.
  Since I'm new to decision trees I would like some assistance.
  I would like to have the prediction (target variable) expressed in a probability (%), not in a binary output.
  How can I achieve this?
  Also I want to play around with samples by inputting the data myself and see what the outcome is.
  For instance: let's take someone who is 40, male etc. and calculate what its survival chance is.
  How can I achieve this?
  I've attached the code below:</p>
  <pre><code>from pandas import read_csv
  from sklearn.model_selection import train_test_split
  from sklearn.tree import DecisionTreeClassifier 
  import pandas as pd
  import random as rnd
  
  filename = '/Users/sef/Downloads/pima-indians-diabetes.csv'
  names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
  dataframe = read_csv(filename, names=names)
  array = dataframe.values
  X = array[:,0:8]
  Y = array[:,8]
  
  X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=1234)
  
  model = DecisionTreeClassifier()
  
  model.fit(X_train, Y_train)
  
  DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
              max_features=None, max_leaf_nodes=None,
              min_impurity_decrease=0.0, min_impurity_split=None,
              min_samples_leaf=1, min_samples_split=2,
              min_weight_fraction_leaf=0.0, presort=False, random_state=None,
              splitter='best')
  
  rnd.seed(123458)
  X_new = X[rnd.randrange(X.shape[0])]
  X_new = X_new.reshape(1,8)
  YHat = model.predict_proba(X_new)
  
  
  df = pd.DataFrame(X_new, columns = names[:-1])
  df[&quot;predicted&quot;] = YHat
  print(df)
  
  </code></pre>