Pickl AI

JULY 7, 2023

random_state=42) # Create a Gradient Boosting classifier clf = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1,

Algorithm 52

Algorithm Machine Learning Categorization Data Science 52

Mlearning.ai

OCTOBER 2, 2023

It uses the ExhaustiveFeatureSelector function using a random forest to do the work. fillna(0), train_labels) train_pred = clf.predict_proba(train_features[filtered_features].fillna(0)) fillna(0)) print('Accuracy on test set: {}' format(roc_auc_score(test_labels, test_pred [:,1]))) This technique can be visualized in the following manner.

Machine Learning 98

Machine Learning Explainability Auto-complete Algorithm 98

Heartbeat

SEPTEMBER 13, 2023

Decision trees can be trained and visualized in rule-based explanations to reveal the underlying decision logic. " LIME (Local Interpretable Model-Agnostic Explanations) " is a popular technique for generating local explanations.

Explainability 52

Explainability Machine Learning Deep Learning Explainable AI 52

Heartbeat

FEBRUARY 22, 2023

Train the traditional machine learning model (Random Forest) using the combined data clf = RandomForestClassifier(n_estimators=100) clf.fit(x_val_with_features, y_val) In this section, a traditional machine learning model, a Random Forest classifier, is being trained on the combined validation set data x_val_with_features and labels y_val.

Deep Learning 52

Deep Learning Machine Learning Convolutional Neural Networks Computer Vision 52

Mlearning.ai

NOVEMBER 20, 2023

clf = LinearRegression().fit(X, Most importantly, we can use the line to predict the y value of any given x input. And prediction is the bread and butter of machine learning. With the popular machine learning package scikit-learn you can run linear regression in a single line of code.

Machine Learning 52

Machine Learning Algorithm AI AI 52

Mlearning.ai

APRIL 1, 2023

type=float) parser.add_argument("--registered_model_name", type=str, help="model name") parser.add_argument("--model", type=str, help="path to model file") args = parser.parse_args() #current_experiment = Run.get_context().experiment py38-cpu/versions/4 #azureml:AzureML-sklearn-1.0-ubuntu20.04-py38-cpu:1

Responsible AI 40

Responsible AI AI AI ML 40

Towards AI

JUNE 5, 2023

In this can, we are using it to prepare data for an XGBoost Classifier, which preprocesses and classifies the text: # Importing the necessary modules and classesfrom sklearn.pipeline import Pipelinefrom sklearn.preprocessing import LabelEncoderfrom xgboost import XGBClassifier# Creating an instance of LabelEncoder classle = LabelEncoder()# Encoding (..)

LLM 52

LLM NLP OpenAI ChatGPT 52

Mlearning.ai

APRIL 13, 2023

Implement a Kernel Function in Python As an example, here is a sample code for using the radial basis function (RBF) kernel in Python using the scikit-learn library: import numpy as np from sklearn.svm import SVC from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split # Load the iris dataset iris = load_iris () X (..)

Data Scientist 52

Data Scientist Machine Learning Neural Network Algorithm 52