From 77b4089ffced22947d0ea5def7b945828ce3e39a Mon Sep 17 00:00:00 2001 From: hathawayj Date: Thu, 29 Feb 2024 16:25:57 +0000 Subject: [PATCH] deploy: c5e7875711029a098860069fc6ccd61684e54029 --- index.html | 2 +- slides/p4/d2/index.html | 21 ++++++++++++--------- 2 files changed, 13 insertions(+), 10 deletions(-) diff --git a/index.html b/index.html index 72206af..cca0a65 100644 --- a/index.html +++ b/index.html @@ -3,4 +3,4 @@

CSE 250: Data Science Programming

Using pandas, Altiar, scikit-learn, and NumPy to program with data

-

Skill Builders

Build skills for the projects.

Slack

Link to Slack signup

Slides

Class material for every day.

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Skill Builders

Build skills for the projects.

Slack

Link to Slack signup

Slides

Class material for every day.

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Day 2: Intro to Machine Learning

Welcome to class!

Announcements

Spiritual thought

Are facts true?

  • How do you distinguish between truth and error?
  • Joshua and Caleb

Building a Decision Tree

Import packages

Splitting the Data

1. Start with packages and data set

We’ll be using some parts of SKLEARN package and the Seaborn package.

# If you haven't already, install scikit-learn and seaborn
+active">Day 2: Intro to Machine Learning
  • Day 1: Intro to ML
  • Week 6-7: Project 3 - Baseball
  • Week 4-5: Project 2 - Flights
  • Week 2-3: Project 1 - Names
  • Week 1: Introduction

    • Day 2: Intro to Machine Learning

    Welcome to class!

    alt text

    Shire Reckoning

    Announcements

    1. Coding Challenge Practice - Thursday, March 7

    Spiritual thought

    Are facts true?

    • How do you distinguish between truth and error?
    • Joshua and Caleb

    Building a Decision Tree

    Import packages

    Splitting the Data

    1. Start with packages and data set

    We’ll be using some parts of SKLEARN package and the Seaborn package.

    # If you haven't already, install scikit-learn and seaborn
 pip install scikit-learn seaborn
 
    from types import GeneratorType
 import pandas as pd
@@ -30,17 +30,20 @@

    4. Split into training and testing sets

    What does the “train_test_split()” function do?

    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = #???, random_state = #???)

    Read the documentation and tell me what is returned?

    Function documentation

    Why do we use “test_size” and “random_state”?

    What is “x” and “y” in the above function example?

    We need to take our data and build the feature and target data objects.

    What columns should we remove from our features (X)?

    What column should we use as our target (y)?

    x = dwellings_ml.filter([#what variables will you use as "features"?])
 y = dwellings_ml[#what variable is the "target"?]
-


    Training a Classifier

    Decision Tree Example

    # create the model
-classifier = DecisionTreeClassifier()
+


    Training a Classifier

    Decision Tree Example

    
+#%%
+# Create a decision tree
+classifier_DT = DecisionTreeClassifier(max_depth = 4)
 
-# train the model
-classifier.fit(x_train, y_train)
+# Fit the decision tree
+classifier_DT.fit(x_train, y_train)
 
-# make predictions
-y_predictions = classifier.predict(x_test)
+# Test the decision tree (make predictions)
+y_predicted_DT = classifier_DT.predict(x_test)
+
+# Evaluate the decision tree
+print("Accuracy:", metrics.accuracy_score(y_test, y_predicted_DT))
 
-# test how accurate predictions are
-metrics.accuracy_score(y_test, y_predictions)

    How to Improve Accuracy

    To improve the accuracy of your model, you could:

    • Change what variables are used in the features (x) data set
    • Change what type of model you are using
    • Tune (aka, “change” or “tweak”) the parameters of the model

    Other Classification Models

    Here are some other models you could try.

    from sklearn.naive_bayes import GaussianNB
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.ensemble import GradientBoostingClassifier