Building a Machine Leaning Model

 Building a Model

• Creating a model is an interactive process:
• Gather the data needed
• Selection of features - collect past observations (houses for sale) and targets (sale process)
• Choice of algorithm - template for relationship between the input and the output
• Selection of values for hyperparameters (tuning the dials of the model to make sure it functions)
• Selection of Loss (cost) function
• Train the model using past data collected
• Evaluate the performance

Feature Selection

• Features - characteristics of the input data
• Important to define feature - interaction of factors that:
• Might influence the solution (years the house is build, city, neighborhood)
• The data that we might be able to collect
• Methods of selection:
• Speak w experts - domain expertise
• Collect data - Visualization
• Past data collection =- statistical correlation
• Collect as much as you can, but narrow down the data - Modeling

Algorithm Selection

• Dependent on the task that needs t be solved
• Best approach - employ different algorithms and train all
• Criteria for algorithm selection:
• Performance Accuracy - expected performance of the model
• Interpretability - how easy/hard it is to interpret and predict the result of the model computation
• Computational Efficiency - computational horsepower measure in relation to the result generation

Model Complexity

Depends on:

• Number of Features
• Algorithm - linear regression vs neuronetworks
• Hyperparameter Values

Bias-Variance Trade off

• Bias - modeling a complex problem using a simple model; the model is incapable of fully capturing the depth and underlying patterns in the data.
• Variance - sensitivity of the module to small fluctuations in the data; ex: interpreting noise as actual patterns

Typically

• Simpler model = higher bias; lower variance
• Complex model = lower bias; higher variance
• Total Error = Bias`2 + Variance + Inherent Error(noise)`2
• Need to be careful Underfitting / Overfitting the model