What is a Learning Curve in machine learning?

Question

I want to know what a learning curve in machine learning is. What is the standard way of plotting it? I mean what should be the x and y axis of my plot?

Answer 1

Some people use "learning curve" to refer to the error of an iterative procedure as a function of the iteration number, i.e., it illustrates convergence of some utility function. In the example below, I plot mean-square error (MSE) of the least-mean-square (LMS) algorithm as a function of the iteration number. That illustrates how quickly LMS "learns", in this case, the channel impulse response.

Answer 2

Example X= Level y=salary

X Y 0 2000 2 4000 4 6000 6 8000

Regression gives accuracy 75% it is a state line polynomial gives accuracy 85% because of the curve

Answer 3

It usually refers to a plot of the prediction accuracy/error vs. the training set size (i.e: how better does the model get at predicting the target as you the increase number of instances used to train it)

Usually both the training and test/validation performance are plotted together so we can diagnose the bias-variance tradeoff (i.e determine if we benefit from adding more training data, and assess the model complexity by controlling regularization or number of features).

Answer 4

use this code to plot :

# Loss Curves
plt.figure(figsize=[8,6])
plt.plot(history.history['loss'],'r',linewidth=3.0)
plt.plot(history.history['val_loss'],'b',linewidth=3.0)
plt.legend(['Training loss', 'Validation Loss'],fontsize=18)
plt.xlabel('Epochs ',fontsize=16)
plt.ylabel('Loss',fontsize=16)
plt.title('Loss Curves',fontsize=16)

# Accuracy Curves
plt.figure(figsize=[8,6])
plt.plot(history.history['acc'],'r',linewidth=3.0)
plt.plot(history.history['val_acc'],'b',linewidth=3.0)
plt.legend(['Training Accuracy', 'Validation Accuracy'],fontsize=18)
plt.xlabel('Epochs ',fontsize=16)
plt.ylabel('Accuracy',fontsize=16)
plt.title('Accuracy Curves',fontsize=16)

note that history = model.fit(...)