How to Calculate Train Accuracy in Python

In Machine Learning, training a model to make predictions can be very useful. However, it’s essential to determine how accurately your model is performing to validate its effectiveness. In this tutorial, we will learn how to calculate train accuracy in Python.

Step 1: Create the “dataset.csv” file

Create a file and put the following data inside:

feature_1, feature_2, feature_3, feature_4, target
1.23, 4.56, 2.87, 0.95, 1
2.11, 3.45, 1.98, 0.76, 0
3.01, 5.67, 2.11, 1.02, 1
4.12, 2.34, 1.45, 0.80, 0
2.67, 3.89, 1.23, 0.55, 0

Step 2: Import Necessary Libraries

Firstly, we will need to import the necessary libraries for the process. Here is a list of the required libraries that we would be importing:

  • numpy: It is used for mathematical computation.
  • pandas: It is used for data processing.
  • sklearn: It contains machine learning algorithms.
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import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn import metrics

Step 3: Load the Dataset

We would be using a hypothetical dataset for this tutorial. If you are using your own dataset, substitute accordingly.

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data = pd.read_csv('dataset.csv')
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values

Step 4: Split the Dataset

Once the data is loaded, we need to split it into two sections, training and test data. The function train_test_split() helps with this. Let’s divide the dataset into 80% training data and 20% test data.

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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

Step 5: Create the Model

After splitting the data, let’s initialize a Logistic Regression model (or any other model of your choice) and fit it with our training data.

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model = LogisticRegression()
model.fit(X_train, y_train)

Step 6: Calculate Train Accuracy

We can then calculate the train accuracy of our model. This is done using the method model.score() which returns the mean accuracy on the given test data and labels.

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train_accuracy = model.score(X_train, y_train)
print(train_accuracy)

The Full Code:

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import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
 
data = pd.read_csv('dataset.csv')
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
 
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
 
model = LogisticRegression()
model.fit(X_train, y_train)
 
train_accuracy = model.score(X_train, y_train)
print(train_accuracy)

Conclusion

That’s it! You have successfully computed the training accuracy of a machine-learning model using Python. It’s important to note that this metric alone may not determine the model’s overall performance, other measures like Precision and Recall, and F1 Score are useful for a more comprehensive evaluation.