CODEWITHSUNDEEP
pythonmachine-learningscikit-learndecision-tree
Harshad
Harshad

Reputation: 25

Not getting the same output as the tutorial

I am trying the Machine Learning tutorial on Kaggle

When they do it, they get the output as: Kaggle outupt

This is my code:

import pandas as pd

pd.set_option('display.max_rows', 5000)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
# Read file from source
data = pd.read_csv(r"C:\Users\harsh\Documents\My Dream\Desktop\Machine Learning\melb_data.csv", skiprows=0)

data = data.dropna(axis=0)

# Column that you want to predict = y
y = data.Price

# Columns that are inputted into the model to make predictions (dependants)
data_features = ['Rooms', 'Bathroom', 'Landsize', 'Lattitude', 'Longtitude']

X = data[data_features]

from sklearn.tree import DecisionTreeRegressor

# Define model. Specify a number for random_state to ensure same results each run
data_model = DecisionTreeRegressor(random_state=1)

# Fit model
data_model.fit(X, y)

My output is just

 Process finished with exit code 0

Whenever I want any output in the Run area, I have to put the function in print()

My question predominantly is:

  1. How do I get the output as the Kaggle output in my PyCharm? Have I configured the IDE correctly?

  2. Why do I need to put the print() function to display results every time? Do I need to do this svery time?

  3. With print(), I get the output as:

    DecisionTreeRegressor(random_state=1)

    What am I missing?

I am using PyCharm 2019.2.6 and Python 3.7 configuration

Upvotes: 0

Views: 43

Answers (1)

thorntonc
thorntonc

Reputation: 2126

Sklearn is not printing out the model parameters. You can print the DecisionTreeRegressor parameters with print(data_model.get_params())

Upvotes: 1

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