Machine Learning for Beginners in Python: How to Generate Text Reports On Performance

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Generate Text Reports On Performance

Preliminaries


/* Load libraries /*
from sklearn import datasets
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report

Load Iris Flower Data


/* Load data */
iris = datasets.load_iris()

/* Create feature matrix */
X = iris.data

/* Create target vector */
y = iris.target

/* Create list of target class names */
class_names = iris.target_names

Create Training And Test Sets


/* Create training and test set */
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)

Train A Logistic Regression Model


/* Create logistic regression */
classifier = LogisticRegression()

/* Train model and make predictions */
y_hat = classifier.fit(X_train, y_train).predict(X_test)

Generate Report


/* Create a classification report */
print(classification_report(y_test, y_hat, target_names=class_names))
             precision    recall  f1-score   support

     setosa       1.00      1.00      1.00        13
 versicolor       1.00      0.62      0.77        16
  virginica       0.60      1.00      0.75         9

avg / total       0.91      0.84      0.84        38

Note: Support refers to the number of observations in each class.

 

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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