kunaalnaik / dsm_knime_machinelearning_intuition

Build intution on Machine Learning Algorithms by using No Node ML tool KNIME

L0 - Machine Learning Intuition using KNIME

Objective

• Learn how to build basic Machine Learning models using KNIME
• Build Top 5 Use Cases in Machine Learning
  • Classification
  • Regression
  • Clustering
  • Forecasting
  • Text Classification

Classification

Classification models are a subset of supervised machine learning . A classification model reads some input and generates an output that classifies the input into some category. For example, a model might read an email and classify it as either spam or not — binary classification.

Regression

Regression is a technique for investigating the relationship between independent variables or features and a dependent variable or outcome. It's used as a method for predictive modelling in machine learning, in which an algorithm is used to predict continuous outcomes.

Next Steps

1/ Preprocessing

• Missing Values Treatment
  • Numerical - Mean, Median, Mode
  • Categorical - Most Frequent, Constant
• Outlier Treatment
  • Numerical - Quartile Method
• Transformation
  • Numerical - Standard Scaler
  • Categorical - Not Required in KNIME
    • Ensure Categories are less

2/ Validation

• Train Test Split
  • 80% Train, 20% Test/Validation