What is an Accuracy Score and How to Check it?
It is common sense that the proportion of right predictions made by a Classification algorithm is the best indicator of how well the algorithm performs. The Accuracy score is based on this same reasoning.
An Accuracy score (or simply Accuracy) is a Classification measure in Machine Learning that represents a percentage of correct predictions made by a model. Due to its simplicity in calculation and interpretation, the measure has found widespread use. Additionally, the performance of the model is quantified by a single number.
If you want to use the Accuracy score to evaluate a Classification model, you will need to have:
Classes that correspond to reality; Predictions made by the model.
Reputed institutes now offer the data science online course.
ACCURACY SCORE FORMULA
You shouldn’t have any trouble making sense of Accuracy since it’s a measure that relies heavily on common sense techniques. To get the Accuracy score, take the number of right guesses and divide it by the total number of predictions made.
Accuracy = Number of correct predictions/Total number of predictions
The more formal formula is the following one.
True Negatives + True Positive
Accuracy = True Positive + False Positive + True Negative + False Negative
True positive, true negative, false positive, and false negative are only few of the words that may be used to represent Accuracy in the Confusion matrix. According to the Confusion matrix page, however, these definitions are typically reserved for binary Classification jobs.
Therefore, the following is the procedure for calculating the Accuracy score in a binary Classification task:
Compile your model’s forecasts;
Calculate the number of True Positives, True Negatives, False Positives, and False Negatives;
For the simple situation of binary data, you may use the Accuracy formula;
And then evaluate the resultant sum.
Basically, that’s all there is to it. Isn’t there also a multiclass scenario? There isn’t a hard and fast method, but you may come close by using the metric’s underlying principles. In the case of a multiclass Classification problem, the accuracy score algorithm is as follows:
Collect your model’s forecasts;
Find out how many guesses were right;
Take that number and divide it by the sum of your predictions; then look at the resulting number.
HOW TO INTERPRET THE ACCURACY SCORE VALUE?
Unlike other cases, the interpretation of Accuracy efficiency metric values is quite simple. Increases in Accuracy occur as more of your forecasts come true. An improved measure would have a greater value. If a model made accurate predictions, it would have a value of 1, and if it didn’t, it would have a value of 0. (if a model did not make a single correct prediction).
Accuracy > 0.9 is a great score, Accuracy > 0.7 is a decent score, and Accuracy 0.7 is a mediocre score, in our opinion. Since your reasoning and the nature of your work may differ significantly from ours, you are free to choose your own cutoffs (for instance, in medicine, you may need an Accuracy score of 0.99+ before considering the task complete).
However, there are two major limitations to this statistic that should be taken into account. Let’s talk about them separately.
ACCURACY SCORE IMBALANCE PROBLEM?
If your set has an abnormally concentrated number of classes in one area, then Accuracy will be completely ineffective. Let’s have a look at a basic illustration.
For instance, we’re interested in measuring how well different email spam filters work. Here at least 100 of the emails are not spam. With a 90% accuracy rate (True Negative = 90, False Positive = 10), our classifier made accurate predictions for 90 of them. In a test with 10 spam emails, the classifier correctly recognized just 5 (True Positive = 5, False Negative = 5). Therefore, the Accuracy rating in this scenario will be:
Roughly, the accuracy is 0.864, or (5 + 90) / (90 + 10 + 5 + 5).
But if we anticipate that every email is legitimate, our Accuracy (True Negative = 100, False Positive = 0, True Positive = 0, False Negative = 10) would improve.
Precision = (100 + 0) (100 + 0) (10 + 0) = 0.909
The second model scores higher on the metrics, but it can’t predict anything. Always make sure your data doesn’t have a class imbalance issue before applying Accuracy.
Fairly, Data Scientists devised the Balanced Accuracy measure to address this issue. For further information, see the relevant section of the sklearn manual.
The data science course fees may go up to INR 4 lakhs.
ACCURACY SCORE BEING UNINFORMATIVE
A further drawback is that Accuracy, when used alone, does not provide a lot of useful information. It does not reveal, for instance, the nature of the mistakes your model makes.
Misclassifications may occur due to either False Positives or False Negatives at a 1% rate (99% Accuracy). When assessing a model’s suitability for a certain application, this kind of data is crucial. Take COVID testing as an example; in this case, it is preferable to have false positives (FPs) rather than false negatives (FNs) (the test says that a person does not have COVID, but he actually does).
However, it is important to remember that depending just on the Accuracy number is a terrible idea, even if this is not a huge issue since it can be solved in a few lines of code by calculating some additional metrics.
CODE IMPLEMENTATION
Because of its widespread use, accuracy score is supported by almost every Machine Learning and Deep Learning library. Three Python code snippets are shown on this page, each one calculating Accuracy.
Scikit-Learn (Sklearn)
With regards to traditional Machine Learning tools, Scikit-learn is by far the most widely used Python package. We predict that Sklearn will be your go-to software for Accuracy calculations (especially, if you are working with the tabular data). Thankfully, it just takes a few lines of code to do.
# Importing the function
from sklearn.metrics import accuracy_score
# Initializing the arrays (multiclass case)
y_pred = [0, 2, 1, 3]
y_true = [0, 1, 2, 3]
# Calculating and printing the result
accuracy_score(y_true, y_pred, normalize=False)
Sklearn’s Accuracy settings go above and beyond its fundamental capabilities. The time and effort saved by using them will be well worth it.
TensorFlow
The Accuracy score method is modified somewhat for use in the area of vision AI. If the predicted class matches the ground truth class and the IoU of the prediction is greater than some threshold, the prediction is considered accurate by various types of segmentors, semantic segmentors, and object detectors (often, a threshold of 0.5 is used).
# Importing the library
import tensorflow as tf
# Calculating the metric value
m = tf.keras.metrics.
Accuracy()
m.update_state([1, 2, 3, 4], [0, 2, 3, 4])
# Printing the result
print(‘Final result: ‘, m.result().
numpy())
PyTorch
!pip install torchmetrics
# Importing the library
import torch
import torchmetrics
from torchmetrics import Accuracy
# Initializing the input tensors
target = torch.tensor([0, 1, 2, 3])
preds = torch.tensor([0, 2, 1, 3])
# Calculating and printing the result
accuracy = Accuracy()
accuracy(preds, target)
It is common sense that the proportion of right predictions made by a Classification algorithm is the best indicator of how well the algorithm performs. The Accuracy score is based on this same reasoning.
An Accuracy score (or simply Accuracy) is a Classification measure in Machine Learning that represents a percentage of correct predictions made by a model. Due to its simplicity in calculation and interpretation, the measure has found widespread use. Additionally, the performance of the model is quantified by a single number.
If you want to use the Accuracy score to evaluate a Classification model, you will need to have:
Classes that correspond to reality; Predictions made by the model.
Reputed institutes now offer the data science online course.
ACCURACY SCORE FORMULA
You shouldn’t have any trouble making sense of Accuracy since it’s a measure that relies heavily on common sense techniques. To get the Accuracy score, take the number of right guesses and divide it by the total number of predictions made.
Accuracy = Number of correct predictions/Total number of predictions
The more formal formula is the following one.
True Negatives + True Positive
Accuracy = True Positive + False Positive + True Negative + False Negative
True positive, true negative, false positive, and false negative are only few of the words that may be used to represent Accuracy in the Confusion matrix. According to the Confusion matrix page, however, these definitions are typically reserved for binary Classification jobs.
Therefore, the following is the procedure for calculating the Accuracy score in a binary Classification task:
Compile your model’s forecasts;
Calculate the number of True Positives, True Negatives, False Positives, and False Negatives;
For the simple situation of binary data, you may use the Accuracy formula;
And then evaluate the resultant sum.
Basically, that’s all there is to it. Isn’t there also a multiclass scenario? There isn’t a hard and fast method, but you may come close by using the metric’s underlying principles. In the case of a multiclass Classification problem, the accuracy score algorithm is as follows:
Collect your model’s forecasts;
Find out how many guesses were right;
Take that number and divide it by the sum of your predictions; then look at the resulting number.
HOW TO INTERPRET THE ACCURACY SCORE VALUE?
Unlike other cases, the interpretation of Accuracy efficiency metric values is quite simple. Increases in Accuracy occur as more of your forecasts come true. An improved measure would have a greater value. If a model made accurate predictions, it would have a value of 1, and if it didn’t, it would have a value of 0. (if a model did not make a single correct prediction).
Accuracy > 0.9 is a great score, Accuracy > 0.7 is a decent score, and Accuracy 0.7 is a mediocre score, in our opinion. Since your reasoning and the nature of your work may differ significantly from ours, you are free to choose your own cutoffs (for instance, in medicine, you may need an Accuracy score of 0.99+ before considering the task complete).
However, there are two major limitations to this statistic that should be taken into account. Let’s talk about them separately.
ACCURACY SCORE IMBALANCE PROBLEM
If your set has an abnormally concentrated number of classes in one area, then Accuracy will be completely ineffective. Let’s have a look at a basic illustration.
For instance, we’re interested in measuring how well different email spam filters work. Here at least 100 of the emails are not spam. With a 90% accuracy rate (True Negative = 90, False Positive = 10), our classifier made accurate predictions for 90 of them. In a test with 10 spam emails, the classifier correctly recognized just 5 (True Positive = 5, False Negative = 5). Therefore, the Accuracy rating in this scenario will be:
Roughly, the accuracy is 0.864, or (5 + 90) / (90 + 10 + 5 + 5).
But if we anticipate that every email is legitimate, our Accuracy (True Negative = 100, False Positive = 0, True Positive = 0, False Negative = 10) would improve.
Precision = (100 + 0) (100 + 0) (10 + 0) = 0.909
The second model scores higher on the metrics, but it can’t predict anything. Always make sure your data doesn’t have a class imbalance issue before applying Accuracy.
Fairly, Data Scientists devised the Balanced Accuracy measure to address this issue. For further information, see the relevant section of the sklearn manual.
The data science course fees may go up to INR 4 lakhs.
ACCURACY SCORE BEING UNINFORMATIVE
A further drawback is that Accuracy, when used alone, does not provide a lot of useful information. It does not reveal, for instance, the nature of the mistakes your model makes.
Misclassifications may occur due to either False Positives or False Negatives at a 1% rate (99% Accuracy). When assessing a model’s suitability for a certain application, this kind of data is crucial. Take COVID testing as an example; in this case, it is preferable to have false positives (FPs) rather than false negatives (FNs) (the test says that a person does not have COVID, but he actually does).
However, it is important to remember that depending just on the Accuracy number is a terrible idea, even if this is not a huge issue since it can be solved in a few lines of code by calculating some additional metrics.
CODE IMPLEMENTATION
Because of its widespread use, accuracy score is supported by almost every Machine Learning and Deep Learning library. Three Python code snippets are shown on this page, each one calculating Accuracy.
Scikit-learn (Sklearn)
With regards to traditional Machine Learning tools, Scikit-learn is by far the most widely used Python package. We predict that Sklearn will be your go-to software for Accuracy calculations (especially, if you are working with the tabular data). Thankfully, it just takes a few lines of code to do.
# Importing the function
from sklearn.metrics import accuracy_score
# Initializing the arrays (multiclass case)
y_pred = [0, 2, 1, 3]
y_true = [0, 1, 2, 3]
# Calculating and printing the result
accuracy_score(y_true, y_pred, normalize=False)
Sklearn’s Accuracy settings go above and beyond its fundamental capabilities. The time and effort saved by using them will be well worth it.
TensorFlow
The Accuracy score method is modified somewhat for use in the area of vision AI. If the predicted class matches the ground truth class and the IoU of the prediction is greater than some threshold, the prediction is considered accurate by various types of segmentors, semantic segmentors, and object detectors (often, a threshold of 0.5 is used).
# Importing the library
import tensorflow as tf
# Calculating the metric value
m = tf.keras.metrics.
Accuracy()
m.update_state([1, 2, 3, 4], [0, 2, 3, 4])
# Printing the result
print(‘Final result: ‘, m.result().
numpy())
PyTorch
!pip install torchmetrics
# Importing the library
import torch
import torchmetrics
from torchmetrics import Accuracy
# Initializing the input tensors
target = torch.tensor([0, 1, 2, 3])
preds = torch.tensor([0, 2, 1, 3])
# Calculating and printing the result
accuracy = Accuracy()
accuracy(preds, target)
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