Data Science 🐍 Classification

The video tutorial covers data preparation and classification techniques, including supervised, unsupervised, and semi-supervised learning. It introduces various classifiers like SVC, logistic regression, KNN, decision trees, and neural networks. The tutorial also includes a practical activity using TC Lab to develop a classifier for determining heater status.

What is the main difference between supervised and unsupervised learning?

• Neither uses labeled data.

• Unsupervised learning uses labeled data, while supervised learning does not.

• Both use labeled data but in different ways.

• Supervised learning uses labeled data, while unsupervised learning does not.

What is semi-supervised learning?

• A method that does not involve any data.

• A method that uses only labeled data.

• A method that uses a mix of labeled and unlabeled data.

• A method that uses only unlabeled data.

In the example of supervised learning, what type of classifier was used to recognize numbers?

• Support Vector Classifier

• Logistic Regression

• K-Nearest Neighbors

• Decision Tree

Which classification method is known for being simple and fast, often used in spam filtering?

• Logistic Regression

• Neural Networks

• Naive Bayes

• K-Nearest Neighbors

Which classifier is known for using more memory but providing accurate results by considering neighboring data points?

• Naive Bayes

• K-Nearest Neighbors

• Support Vector Classifier

• Logistic Regression

What is a common use of logistic regression in machine learning?

• As an output layer in neural networks

• For clustering data

• For unsupervised learning

• For generating random forests

What is a key feature of the Gaussian mixture model in unsupervised learning?

• It requires labeled data.

• It provides a continuous prediction of certainty.

• It is only used for clustering images.

• It uses decision trees for classification.

What challenge does unsupervised learning face compared to supervised learning?

• It requires more labeled data.

• It struggles with data that is too homogeneous.

• It cannot be used for clustering.

• It is slower than supervised learning.

What is the purpose of the temperature control lab activity?

• To develop a classifier for predicting weather patterns.

• To evaluate the performance of different classifiers in predicting heater status.

• To compare different clustering algorithms.

• To demonstrate the use of neural networks in temperature prediction.

In the temperature control lab activity, what additional feature was suggested to improve classifier performance?

• Wind Speed

• Pressure

• Temperature 2

• Humidity