teach my machine

Regression And Classification

Simply put regression is predicting values (price, speed, etc) and classification is predicting classes (category, type, yes/no, true / false, color, etc).

How To Save Trained Model

Scikit-Learn offers a handy module for saving and loading trained models:

from sklearn.externals import joblib

To save model:

joblib.dump(model, "model.pkl")

Evaluating Model Performance via Cross-Validation

To measure regression model performance, we use RMSE which stands for Root Mean Squared Error. 
You can do this easily with Scikit-Learn, but at first, lets train our model:

How To Split Pandas DataSet into Features and Labels

Let’s say you have a training data set as pandas data frame train_set. Before feeding it to a machine learning algorithm, you need to split it into features and labels (aka answers).

Visualize Pandas Data Easily With Matplotlib

Building a Matplotlib graph from your Pandas DataFrame is as easy as calling plot() method on your data:

data.plot(kind=”bar”, x=”years”, y=”income”)

Splitting test and train data based on strata

When we split a dataset into test and train sets, we often use different tactics for splitting. Very often methods are based on random records picking and putting them in different sets.

Classification vs Regression

Classification predicts a categorical response (true or false, red or green, spam or not spam, big/medium/small, etc), while regression predicts a continuous response ie a number (height, weight, duration).

Basic machine learning terminology

One of the most confusing to beginners things in ML is its terminology. Every ML concept has several synonyms. What’s makes things even worse, each ML course or book uses different combination of these. There’s no common standard.