Unsupervised vs. Supervised Learning

Unsupervised vs. Supervised Learning

I just started my initial steps into data science and machine learning, and, got introduced to “Supervised Learning” techniques as “Classifiers (Decisiontreeclassifer from sklearn kit).

I just started my initial steps into data science and machine learning, and, got introduced to “Supervised Learning” techniques as “Classifiers (Decisiontreeclassifer from sklearn kit), and on the unsupervised learning, with “Clustering.”

In this case, we are using the dataset “Breast cancer — Wisconsin” and set the following objective:

*a) *Perform clustering (k-means), use evaluation methods like silhouette score and WSS (within the sum of squares) to find optimal clusters,

b) Perform a Decisiontreeclassifier model, and the traditional train versus test samples and evaluate the model with ROC/AUC

*c) *Compare the clustering model output with the efficiency of Decisiontreeclassifer model outcome

The comparison outcome, presented a surprise to me, were without the target/class variables, the accuracy with just clustering, was close to 95 % match to the actual class variables in the data set, better than Supervised learning (with 70: 30, train to test split up, the accuracy was 92 % ). Now, does this mean it will work for larger samples also, is to be validated for larger data sets?

Let us get started — Data insights :

Features are a digitized image compilation of a fine needle aspirate (FNA) of a breast mass. They describe the characteristics of the cell nuclei present in the image.

Total rows — 569, columns — 32 (including class variable, called diagnosis, with the outcome as Malignant (M) and Benign (B).

decision-tree-classifier k-means-clustering confusion-matrix unsupervised-learning deep learning

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