Training, Test set size and Cross Validation -->

[github-actions]

Training, Test set size and Cross Validation -->

Training, Test set size and Cross Validation

BrownFall20/notes/2020-10-23.md

Line 92 in 6eba0a3

<!-- annotate: Training, Test set size and Cross Validation -->

    extension: .md
    format_name: myst
    format_version: 0.12
    jupytext_version: 1.6.0
kernelspec:
  display_name: Python 3
  language: python
  name: python3
---

```{raw-cell}
# Class 20: Decision Trees and Cross Validation


1. Share your favorite beverage (or say hi) in the zoom chat
1. log onto prismia
1. Accept assignment 7

Assignment 7

Make a plan with a group:

• what methods do you need to use in part 1?
• try to outline with psuedocode what you'll do for part 2 & 3

Share any questions you have.

Followup:

1. assignment clarified to require 3 values for the parameter in part 2
2. more tips on finding data sets added to assignment text

+++

Complexity of Decision Trees

# %load http://drsmb.co/310
import pandas as pd
import seaborn as sns
import numpy as np
from sklearn import tree
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
d6_url = 'https://raw.githubusercontent.com/rhodyprog4ds/06-naive-bayes/main/data/dataset6.csv'

df6= pd.read_csv(d6_url,usecols=[1,2,3])
df6.head()

X_train, X_test, y_train,  y_test = train_test_split(df6.values[:,:2],df6.values[:,2],
                                                     train_size=.8)

dt = tree.DecisionTreeClassifier(min_samples_leaf = 10)
dt.fit(X_train,y_train)

print(tree.export_text(dt))

dt2 = tree.DecisionTreeClassifier(min_samples_leaf = 50)
dt2.fit(X_train,y_train)

print(tree.export_text(dt2))

dt2.score(X_test,y_test)

dt.score(X_test,y_test)

df6.shape

Training, Test set size and Cross Validation

dt3 = tree.DecisionTreeClassifier()
dt3.fit(df6.values[:-1,:2],df6.values[:-1,2],)

print(tree.export_text(dt3))

dt4 = tree.DecisionTreeClassifier(max_depth=2)
cv_scores = cross_val_score(dt4,df6.values[:,:2],df6.values[:,2],cv=100 )
cv_scores

np.mean(cv_scores)

3aff1d06465933a7e184d27ad6430b6f39a6eb22