python plotエラー|: Jupyter notebook上でPythonを実行しています。

python plotエラー|: Jupyter notebook上でPythonを実行しています。

交差検証を行い、True Positive rateを求め、その値をプロットしたいです。 #レポート課題#データの読み込みimport numpy as npimport pandas.

Jupyter notebook上でPythonを実行しています。 

交差検証を行い、True Positive rateを求め、その値をプロットしたいです。

import numpy as np
import pandas as pd
pima_tr = pd.read_csv('data2/pima_tr.csv' , encoding='UTF-8' , index_col=0)
pima_te = pd.read_csv('data2/pima_te.csv' , encoding='UTF-8' , index_col=0)
group_data = pd.concat([pima_tr, pima_te], ignore_index = True)

from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import confusion_matrix

## データのスケーリングとtrainデータとtestデータに分ける
X = preprocessing.scale(group_data[["npreg","glu","bp","skin","bmi","ped","age"]])
y = group_data.type
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, train_size=0.7)

knn = KNeighborsClassifier(), y_train)

from sklearn import linear_model
clf = linear_model.LogisticRegression()    

neighbors = list(range(2, 50))
mean_score= list()
for k in neighbors:
    scores = cross_val_score(clf, X, y, cv=k)
    y_pred = knn.predict(X_train)
    cmat = confusion_matrix(y_train, y_pred)
    #True Positive rateの計算

#最適なk(True Positive rateの計算が最大)の表示
import matplotlib.pyplot as plt
%matplotlib inline
optimal_k = neighbors[tpr_scores.index(max(filter(lambda v: v <1 , tpr_scores)))]
print("The best number of k is %d." % optimal_k)

## 結果の可視化
fig, ax = plt.subplots(figsize=(10, 6))
ax.plot(neighbors, tpr_scores)
ax.set_xlabel('Number of Neighbors K')
ax.set_ylabel('True Positive rate')


Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

Python Tricks Every Developer Should Know

In this tutorial, you’re going to learn a variety of Python tricks that you can use to write your Python code in a more readable and efficient way like a pro.

How to Remove all Duplicate Files on your Drive via Python

Today you're going to learn how to use Python programming in a way that can ultimately save a lot of space on your drive by removing all the duplicates. We gonna use Python OS remove( ) method to remove the duplicates on our drive. Well, that's simple you just call remove ( ) with a parameter of the name of the file you wanna remove done.

Basic Data Types in Python | Python Web Development For Beginners

In the programming world, Data types play an important role. Each Variable is stored in different data types and responsible for various functions. Python had two different objects, and They are mutable and immutable objects.

How To Compare Tesla and Ford Company By Using Magic Methods in Python

Magic Methods are the special methods which gives us the ability to access built in syntactical features such as ‘<’, ‘>’, ‘==’, ‘+’ etc.. You must have worked with such methods without knowing them to be as magic methods. Magic methods can be identified with their names which start with __ and ends with __ like __init__, __call__, __str__ etc. These methods are also called Dunder Methods, because of their name starting and ending with Double Underscore (Dunder).

The Basics of Python OS Module

The OS module is a python module that provides the interface for interacting with the underlying operating system that Python is running.