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How to build a Machine Learning Model?

Pickl AI

Machine Learning models play a crucial role in this process, serving as the backbone for various applications, from image recognition to natural language processing. In this blog, we will delve into the fundamental concepts of data model for Machine Learning, exploring their types. What is Machine Learning?

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Machine Learning for Optimal Performance in AngularJS Development

Mlearning.ai

Using different machine learning algorithms for performance optimization: Several machine learning algorithms can be used for performance optimization, including regression, clustering, and decision trees. Decision tree algorithms can be used to identify performance bottlenecks and suggest optimization strategies.

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Predictive Analytics: 4 Primary Aspects of Predictive Analytics

Smart Data Collective

Data Sourcing. Fundamental to any aspect of data science, it’s difficult to develop accurate predictions or craft a decision tree if you’re garnering insights from inadequate data sources. Objectives and Usage.

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Supervised learning vs Unsupervised learning

Pickl AI

Significantly, Supervised Learning is practical in two types of tasks- Classification: the goal is to predict a categorical label for each input data point Regression: the goal is to predict a continuous value. It includes various algorithms like linear regression, logistic regression, decision trees, bayesian logic, etc.

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Eager Learning and Lazy Learning in Machine Learning: A Comprehensive Comparison

Pickl AI

It constructs a hyperplane to separate different classes during training and uses it to make predictions on new data. Decision Trees : Decision Trees are another example of Eager Learning algorithms that recursively split the data based on feature values during training to create a tree-like structure for prediction.

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How To Use ML for Credit Scoring & Decisioning

phData

With a modeled estimation of the applicant’s credit risk, lenders can make more informed decisions and reduce the occurrence of bad loans, thereby protecting their bottom line. The model learns from these labels to predict the outcome of new, unseen data. loan default or not).

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Machine Learning Model Training Mistakes: How to avoid them

Mlearning.ai

Metrics for ML — Not looking at the model  — Explaining a ML model can help in understanding how it works and makes predictions. Techniques such as feature importance and decision trees can help you gain insights into the inner workings of a model.