English: Calibration is a post-processing operation of the predicted probabilities to match them to the true probability of the target distribution. A classifier for example may output predictions that correctly rank order options or reflect the classifiers relative confidence between two options, \(P(Dog|X)=0.9\), \(P(Cat|X)=0.2\), which would be useful for assigning a final thresholded label but not for estimating the true distribution of dogs and cats in the data.
Attaching package: 'arrow'
The following object is masked from 'package:utils':
timestamp
import numpy as nptoy_vector_numeric = np.array([1,2,3,4,5])toy_vector_character = np.array(['a','b','c','d','e'])toy_list = ['a','1',True,['red','green']]toy_dictionary = { 'a':1 , 'b':2, 'c':3}from jax import numpy as jnptoy_vector_numeric_jax = jnp.array([1,2,3,4,5])#toy_vector_character_jax = jnp.array(['a','b','c','d','e']) #only numeric is allowed in jax
WARNING:jax._src.lib.xla_bridge:No GPU/TPU found, falling back to CPU. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)
import pandas as pdtoy_df = pd.DataFrame(data={'id': ['unit1','unit2','unit3'], 'y': [1, 2, 3], 'x': [3, 2, 1]})import torchimport tensorflow as tfimport pyarrow as pa
library(DBI)# Create an ephemeral in-memory RSQLite database#con <- dbConnect(RSQLite::SQLite(), dbname = ":memory:")#dbListTables(con)#dbWriteTable(con, "mtcars", mtcars)#dbListTables(con)#Configuration failed because libpq was not found. Try installing:#* deb: libpq-dev libssl-dev (Debian, Ubuntu, etc)#install.packages('RPostgres')#remotes::install_github("r-dbi/RPostgres")#Took forever because my file permissions were broken#pg_lsclustersrequire(RPostgres)# Connect to the default postgres database#I had to follow these instructions and create both a username and database that matched my ubuntu name#https://www.digitalocean.com/community/tutorials/how-to-install-postgresql-on-ubuntu-20-04-quickstartcon_Postgres <-dbConnect(RPostgres::Postgres())
DROPTABLEIFEXISTS toy_df;
CREATETABLEIFNOTEXISTS toy_df (idvarchar(5), y INTEGER, x INTEGER);
INSERTINTO toy_df (id, y, x)VALUES ('unit1',1,3), ('unit2',2,2), ('unit3',3,1);