# Plot The Learning Curve

## Preliminaries

```
/* Load libraries */
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_digits
from sklearn.model_selection import learning_curve
```

## Load Digits Dataset

```
/* Load data */
digits = load_digits()
/* Create feature matrix and target vector */
X, y = digits.data, digits.target
```

## Plot Learning Curve

```
/* Create CV training and test scores for various training set sizes */
train_sizes, train_scores, test_scores = learning_curve(RandomForestClassifier(),
X,
y,
# Number of folds in cross-validation
cv=10,
# Evaluation metric
scoring='accuracy',
# Use all computer cores
n_jobs=-1,
# 50 different sizes of the training set
train_sizes=np.linspace(0.01, 1.0, 50))
/* Create means and standard deviations of training set scores */
train_mean = np.mean(train_scores, axis=1)
train_std = np.std(train_scores, axis=1)
/* Create means and standard deviations of test set scores */
test_mean = np.mean(test_scores, axis=1)
test_std = np.std(test_scores, axis=1)
/* Draw lines */
plt.plot(train_sizes, train_mean, '--', color="#111111", label="Training score")
plt.plot(train_sizes, test_mean, color="#111111", label="Cross-validation score")
/* Draw bands */
plt.fill_between(train_sizes, train_mean - train_std, train_mean + train_std, color="#DDDDDD")
plt.fill_between(train_sizes, test_mean - test_std, test_mean + test_std, color="#DDDDDD")
/* Create plot */
plt.title("Learning Curve")
plt.xlabel("Training Set Size"), plt.ylabel("Accuracy Score"), plt.legend(loc="best")
plt.tight_layout()
plt.show()
```

# Python Example for Beginners

## Two Machine Learning Fields

There are two sides to machine learning:

**Practical Machine Learning:**This is about querying databases, cleaning data, writing scripts to transform data and gluing algorithm and libraries together and writing custom code to squeeze reliable answers from data to satisfy difficult and ill defined questions. It’s the mess of reality.**Theoretical Machine Learning**: This is about math and abstraction and idealized scenarios and limits and beauty and informing what is possible. It is a whole lot neater and cleaner and removed from the mess of reality.

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