CoderzColumn : Machine Learning Tutorials (Page: 4)

Machine Learning Tutorials

The term 'machine learning' (ML) describes a system's capacity to gather and synthesize knowledge through extensive observation, as well as to develop and extend itself by picking up new information rather than having it preprogrammed into it. At CoderzColumn, you get a glimpse of the vast Machine Learning Field. We cover various concepts through tutorials. The concepts are:

  • Visualize ML Metrics
  • Gradient Boosted Decision Trees
  • Interpret Predictions Of ML Models
  • Hyperparameters Tuning / Optimization

For an in-depth understanding of the above concepts, check out the sections below.

Recent Machine Learning Tutorials

Scikit-Learn - Feature Selection

Scikit-Learn - Feature Selection

Sunny Solanki  Sunny Solanki
May 25, 2020 ~30 mins
Scikit-Learn - Cross-Validation & Hyperparameter Tuning Using GridSearch

Scikit-Learn - Cross-Validation & Hyperparameter Tuning Using GridSearch

Sunny Solanki  Sunny Solanki
May 24, 2020 ~30 mins
Scikit-Learn - Data Preprocessing [Scaling, Imputation & One-Hot Encoding]

Scikit-Learn - Data Preprocessing [Scaling, Imputation & One-Hot Encoding]

Sunny Solanki  Sunny Solanki
May 23, 2020 ~30 mins
Scikit-Learn - Feature Extraction from Text Data

Scikit-Learn - Feature Extraction from Text Data

Sunny Solanki  Sunny Solanki
Jan 30, 2020 ~45 mins
Scikit-Learn - Creating ML Processing Pipeline

Scikit-Learn - Creating ML Processing Pipeline

Sunny Solanki  Sunny Solanki
Jan 22, 2020 ~20 mins
Scikit-Learn - Unsupervised Learning : Clustering

Scikit-Learn - Unsupervised Learning : Clustering

Sunny Solanki  Sunny Solanki
Jan 10, 2020 ~20 mins
Scikit-Learn - Supervised Learning : Regression

Scikit-Learn - Supervised Learning : Regression

Sunny Solanki  Sunny Solanki
Jan 07, 2020 ~30 mins
Scikit-Learn - Supervised Learning : Classification

Scikit-Learn - Supervised Learning : Classification

Sunny Solanki  Sunny Solanki
Jan 02, 2020 ~30 mins

Subscribe to Our YouTube Channel

YouTube SubScribe

Tutorial Categories

Artificial Intelligence (83)
Data Science (84)
Digital Marketing (8)
Machine Learning (38)
Python (131)

Newsletter Subscription

Visualize Machine Learning Metrics

Visualize Machine Learning Metrics

Once our Machine Learning model is trained, we need some way to evaluate its performance. We need to know whether our model has generalized or not.

For this, various metrics (confusion matrix, ROC AUC curve, precision-recall curve, silhouette Analysis, elbow method, etc) are designed over time. These metrics help us understand the performance of our models trained on various tasks like classification, regression, clustering, etc.

Python has various libraries (scikit-learn, scikit-plot, yellowbrick, interpret-ml, interpret-text, etc) to calculate and visualize these metrics.

Scikit-Learn: ML Model Evaluation Metrics

Scikit-Learn: ML Model Evaluation Metrics
scikit-plot

scikit-plot
yellowbrick

yellowbrick
interpret-ml

interpret-ml
interpret-text

interpret-text
Interpret Predictions Of ML Models

Interpret Predictions Of ML Models

After training ML Model, we generally evaluate the performance of model by calculating and visualizing various ML Metrics (confusion matrix, ROC AUC curve, precision-recall curve, silhouette Analysis, elbow method, etc).

These metrics are normally a good starting point. But in many situations, they don’t give a 100% picture of model performance. E.g., A simple cat vs dog image classifier can be using background pixels to classify images instead of actual object (cat or dog) pixels.

In these situations, our ML metrics will give good results. But we should always be a little skeptical of model performance.

We can dive further deep and try to understand how our model is performing on an individual example by interpreting results. Various algorithms have been developed over time to interpret predictions of ML models and many Python libraries (lime, eli5, treeinterpreter, shap, etc) provide their implementation.

LIME

LIME
Shap Values

Shap Values
eli5

eli5
treeinterpreter

treeinterpreter
dice-ml

dice-ml
Hyperparameters Tuning / Optimization

Hyperparameters Tuning / Optimization

Machine Learning models generally have many parameters that need to be tuned to get the best performing model. E.g., a decision tree has parameters like tree depth, min samples per leaf, maximum leaf nodes, criteria to evaluate split, etc whose different values can be tried to get the best-performing decision tree model.

These parameters of ML models are generally referred to as Hyperparameters. Over the years, various approaches have been developed to get best performing Hyperparameters for ML Model. The process of finding best performing Hyperparameters is referred to as Hyperparameters tuning or Hyperparameters Optimization.

Python has many libraries (optuna, hyperopt, scikit-optimize, scikit-learn, etc) that let us perform Hyperparameters tuning to find best settings for our model.

optuna

optuna
scikit-optimize

scikit-optimize
hyperopt

hyperopt
bayes_opt

bayes_opt
Gradient Boosting

Gradient Boosting

Gradient boosting is a machine learning algorithm based on an ensemble of estimators and is used for regression and classification tasks. The ensemble consists of list of weak predictors / estimators whose predictions are combined to make final model predictions.

Majority of the time, these weak predictors are decision trees, and an algorithm is referred to as gradient-boosted trees or gradient-boosted decision trees. They are best suited for structured tabular datasets.

Python has many libraries (XGBoost, CatBoost, LightGBM, scikit-learn, etc) that provide an implementation of gradient boosting. Apart from implementation, these libraries provide many extra features like parallelization, GPU training, distributed training, command line training / evaluation, higher accuracy, etc.

XGBoost

XGBoost
CatBoost

CatBoost
lightGBM

lightGBM
Scikit-Learn: Gradient Boosting

Scikit-Learn: Gradient Boosting