LU1- Recap
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What are the four types of Data Analytics?
Descriptive Diagnostic Predictive Prescriptive
Name 5 statistical techniques for Data Analysis
Linear regression Classification Resampling methods Tree based methods Unsupervised learning
What is Linear Regression?
Linear Regression is the technique that is used to predict a target variable by providing the best linear relationship among the dependent and independent variable where best fit indicates the sum of all the distances amidst the shape and actual observations at each data point is as minimum as achievable.
What is classification
Classification allocates specific categories to a collection of data for making more spesific predictions and analysis.
Name two types of classification techniques
Logistic Regression Discriminant Analysis
What is Logistic Regression?
A regression analysis technique to perform when the dependent variable is binary. It is a predictive analysis that is utilized for explaining data and the connection amongst one dependent variable and other nominal independent variables.
Name two resampling techniques
Bootstrapping Cross- Validation
What is bootstrapping?
It operates through sampling with replacement from the actual data and accounts the “not selected” data points as test samples.
What is Cross-Validation?
This technique is used in order to validate the model performance, and can be executed by dividing the training data into K parts. During cross validation execution, the K-1 part can be considered as training and the rest made out part acts as a test set. Up to K times, the process is repeated and then the average of K scores is accepted as performance estimation
When does Undersampling take place?
When the majority of the class is copied
When does oversampling take place?
When the minority of the class gets copied
Name 3 Unsupervised learning algorithms
Principal component Analysis K-Means Clustering Hierarchical Clustering
What is Principal component analysis?
recognising a linear-set of the mutually uncorrelated blend of features having maximum variance. Also, it helps in acquiring latent interaction among the variables in an unsupervised framework.
What is Machine Learning?
Machine Learning is the adoption of mathematical and or statistical models in order to get customized knowledge about data for making foresight.
Name an unsupervised machine-learning technique
Clustering
What are Latent variable models?
Latent variable models are commonly used for data preprocessing, such as reducing the number of features in a dataset (dimensionality reduction) or decomposing the dataset into multiple components.
What is Clustering
A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior.
what type of learning( supervised/ unsupervised) makes use of clustering?
unsupervised
what type of learning( supervised/ unsupervised) makes use of Classification?
Supervised
what type of learning( supervised/ unsupervised) makes use of Regression?
Supervised
What type of machine learning type makes use of labelled input and output data during the training phase of the machine learning lifecycle
Supervised learning
To be able to classify new and unseen datasets and predict outcomes, what does a supervised learning model need to learn
relationship between input and output data
What machine learning is where we have input variables (X) and an output variable (Y)
Supervised learning
why do we call it supervised learning?
Because part of the approach requires human oversight