Inertia measures how well a dataset was clustered by K-Means. It is calculated by measuring the distance between each data point and its centroid, squaring this distance, and summing these squares across one cluster.
A good model is one with low inertia AND a low number of clusters (
K). However, this is a tradeoff because as
K increases, inertia decreases.
To find the optimal
K for a dataset, use the Elbow method; find the point where the decrease in inertia begins to slow.
K=3 is the “elbow” of this graph.
Patterns and structure can be found in unlabeled data using unsupervised learning, an important branch of machine learning. Clustering is the most popular unsupervised learning algorithm; it groups data points into clusters based on their similarity. Because most datasets in the world are unlabeled, unsupervised learning algorithms are very applicable.
Possible applications of clustering include:
K-Means is the most popular clustering algorithm. It uses an iterative technique to group unlabeled data into K clusters based on cluster centers (centroids). The data in each cluster are chosen such that their average distance to their respective centroid is minimized.
Repeat Steps 2 and 3 until points don’t move between clusters and centroids stabilize.
sklearn, is a machine learning library for Python that has a K-Means algorithm implementation that can be used instead of creating one from scratch.
To use it:
KMeans() method from the
sklearn.cluster library to build a model with
Fit the model to the data samples using
Predict the cluster that each data sample belongs to using
.predict() and store these as
from sklearn.cluster import KMeansmodel = KMeans(n_clusters=3)model.fit(data_samples)labels = model.predict(data_samples)
One of the most important first steps when working with a dataset is to inspect the variable types, and identify relevant variables. An efficient method to use when inspecting variables is the
.head() method which will return the first rows of a dataset.
When working with nominal categorical variables in Python, it can be useful to use One-Hot Encoding, which is a technique that will effectively create binary variables for each of the nominal categories. This encodes the variable without creating an order among the categories. To one-hot encode a variable in a pandas dataframe, we can use the
df = pd.get_dummies(data = df, columns= ['column1', 'column2')