Feature Selection vs Feature Engineering

Aspect	Feature Selection	Feature Engineering
Purpose	Choose relevant features	Create informative features
Objective	Improve model performance by eliminating irrelevant or redundant features	Improve model's ability to capture patterns and relationships
Techniques	Correlation analysis, mutual information, feature importance scores, recursive feature elimination	Scaling, one-hot encoding, interaction terms, mathematical operations, date-related feature extraction
Automation	Can often be automated using statistical methods and algorithms	May require domain knowledge and human expertise
Examples	SelectKBest, SelectFromModel, Recursive Feature Elimination (RFE)	One-hot encoding, polynomial feature creation, date-related feature extraction

Comments

Data Wrangling vs EDA

Aspect Data Wrangling (Data Preprocessing) Exploratory Data Analysis (EDA) Objective Prepare raw data for modeling by cleaning, transforming, and formatting it appropriately. Explore and understand the data to gain insights, identify patterns, and make decisions on data handling and modeling. Order Typically performed as a preliminary step before EDA. Usually conducted after data wrangling to further investigate data characteristics. Data Handling Focuses on data cleaning, filling missing values, encoding categorical variables, and scaling features. Involves data visualization, statistical analysis, and summary statistics to uncover patterns, relationships, and anomalies. Techniques Techniques include imputation, outlier detection, feature scaling, and one-hot encoding. Techniques include histograms, scatter plots, box plots, correlation matrices, and descriptive statistics. Data Transformation Involves structural changes to the dataset, such as feature engineering, data normaliz...

What is Tensor Parallelism and relationship between Buffer and GPU

Tensor Parallelism in GPU Tensor parallelism is a technique used to distribute the computation of large tensor operations across multiple GPUs or multiple cores within a GPU . It is an essential method for improving the performance and scalability of deep learning models, particularly when dealing with very large models that cannot fit into the memory of a single GPU. Key Concepts Tensor Operations : Tensors are multidimensional arrays used extensively in deep learning. Common tensor operations include matrix multiplication, convolution, and element-wise operations. Parallelism : Parallelism involves dividing a task into smaller sub-tasks that can be executed simultaneously. This approach leverages the parallel processing capabilities of GPUs to speed up computations. How Tensor Parallelism Works Splitting Tensors : The core idea of tensor parallelism is to split large tensors into smaller chunks that can be processed in parallel. Each chunk is assigned to a different GP...

Infrastructure limitations ESX Server 4 vs ESXI 5

Some limitations in ESX Server 4 may constrain the design of data centers:[28][29] • Guest system maximum RAM: 255 GB • Host system maximum RAM: 1 TB[28] • Number of hosts in a high availability cluster: 32 • Number of Primary Nodes in ESX Cluster high availability: 5 • Number of hosts in a Distributed Resource Scheduler cluster: 32 • Maximum number of processors per virtual machine: 8 • Maximum number of processors per host: 160 • Maximum number of cores per processor: 12 • Maximum number of virtual machines per host: 320 • VMFS-3 limits files to 262,144 (218) blocks, which translates to 256 GB for 1 MB block sizes (the default) or up to 2 TB for 8 MB block sizes.[30] However, on a VMFS Boot drive, it is usually very difficult to use anything other than 1 MB Block size [31]. With ESXI 5 there has been some changes to these limits[32] • Guest system maximum RAM: 1 TB • Host system maximum RAM: 2 TB • Number of hosts in a high availability cluster: 32 • Maximum number ...

An Architect's vision

Search This Blog