What is the Accuracy in Machine Learning (Python Example)

Accuracy is the measure of how well a machine learning model can predict outcomes on new data. It’s calculated by dividing the number of correct predictions by the total number of predictions made. The result is a percentage that represents the model’s accuracy.

Why is Accuracy Important?

Accuracy is important because it determines how useful a machine learning model is in practice. If a model has low accuracy, it won’t be able to make accurate predictions on new data, rendering it useless. On the other hand, a model with high accuracy can be used to make predictions with confidence, leading to better decision-making and improved outcomes.

How to Calculate Accuracy?

To calculate accuracy, you first need to split your dataset into training and testing data. Then you train your model on the training data and evaluate its performance on the testing data. You can calculate accuracy by comparing the predicted values to the actual values and counting the number of correct predictions.

How to Evaluate the Accuracy in Python

To evaluate the accuracy of a machine learning model in Python, use the score() method on the trained machine learning model. In this case with will apply the score() method to the fitted knn classifier object.

Then, we’ll plot a heatmap showing off the confusion matrix.

import seaborn as sns
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import confusion_matrix
import matplotlib.pyplot as plt 
# Load the Iris dataset
iris = load_iris()

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=0)

# Train a KNN classifier with k=5
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)

# Predict the labels of the test set
y_pred = knn.predict(X_test)

# Calculate the accuracy of the model
accuracy = knn.score(X_test, y_test)

print('Accuracy:', accuracy)

# Create a confusion matrix using Seaborn
sns.set(font_scale=1.4)
sns.heatmap(confusion_matrix(y_test, y_pred), annot=True, cmap='Blues')
plt.xlabel('Predicted')
plt.ylabel('Actual')
plt.title('Confusion Matrix')
plt.show()

How to Improve the Accuracy of Machine Learning Models

To improve the accuracy of machine learning models, perform a number of the following tasks:

Feature Selection
Feature Engineering
Model Selection

Hyperparameter Tuning
Cross-Validation

Feature Selection

Selecting the right features is critical for improving accuracy. You should choose features that are relevant to the problem you’re trying to solve.

Feature Engineering

Feature engineering is the process of transforming raw data into features that are more meaningful to a machine learning model. This can include things like scaling, normalization, and one-hot encoding.

Model Selection

Choosing the right model is important for improving accuracy. Different models work better for different types of problems, so it’s important to choose the one that’s best suited for your problem.

Hyperparameter Tuning

Hyperparameters are the parameters that are set before training a model. Tuning these parameters can significantly improve accuracy.

Cross-Validation

Cross-validation is a technique used to evaluate the performance of a model. It involves splitting the dataset into multiple parts and training and testing the model on each part.

This code loads the Iris dataset, splits it into training and testing sets, trains a KNN classifier with k=5, predicts the labels of the test set, calculates the accuracy of the model, creates a confusion matrix using Seaborn, and finally visualizes the accuracy of the model using a barplot.

Important Concepts in Accuracy

Confusion matrix

Precision, recall, F1 score
ROC curve
Bias-variance tradeoff

Cross-validation
Overfitting and underfitting
Ensemble methods

To Know Before You Learn Accuracy

Basics of machine learning
Supervised learning
Classification and regression

Model evaluation metrics
Data preprocessing techniques
Model selection techniques

Basic programming skills in Python

What’s Next?

Precision, recall, F1 score
ROC curve

Confusion matrix
Bias-variance tradeoff
Cross-validation techniques

Model selection techniques
Hyperparameter tuning
Ensemble methods

Useful Python Libraries for Accuracy

scikit-learn: accuracy_score, confusion_matrix
numpy: mean
pandas: read_csv

matplotlib: pyplot, figure

Datasets useful for Accuracy Testing

Iris


# Python example
from sklearn.datasets import load_iris
iris = load_iris()
X, y = iris.data, iris.target

MNIST


# Python example
from sklearn.datasets import fetch_openml
mnist = fetch_openml('mnist_784')
X, y = mnist['data'], mnist['target']

Wine


# Python example
from sklearn.datasets import load_wine
wine = load_wine()
X, y = wine.data, wine.target

Relevant Entities

Entity	Properties
Accuracy	The measure of how well a machine learning model can predict outcomes on new data
Training Data	The dataset used to train a machine learning model
Testing Data	The dataset used to evaluate a machine learning model’s performance
Feature Selection	The process of selecting the most relevant features for a machine learning model
Feature Engineering	The process of transforming raw data into features that are more meaningful to a machine learning model
Model Selection	The process of selecting the best machine learning model for a given problem
Hyperparameter Tuning	The process of optimizing the parameters of a machine learning model to improve its performance
Cross-Validation	A technique used to evaluate the performance of a machine learning model by splitting the dataset into multiple parts and training and testing the model on each part

Conclusion

Accuracy is the ultimate metric for machine learning models. It measures how well a model can predict outcomes on new data. Improving accuracy is crucial for making accurate predictions and better decision-making. By following the tips mentioned above, you can improve the accuracy of your machine learning models and make better predictions.

sources

Towards Data Science: “Accuracy, Precision, Recall or F1?” https://towardsdatascience.com/accuracy-precision-recall-or-f1-331fb37c5cb9
Scikit-learn documentation: “Model selection: choosing estimators and their parameters” https://scikit-learn.org/stable/tutorial/statistical_inference/model_selection.html
Machine Learning Mastery: “A Gentle Introduction to Bias-Variance Trade-Off in Machine Learning”

Kaggle: “How to measure and solve bias and variance”
Stanford University CS229: “Machine Learning Cheat Sheet – Model Evaluation”