lucifermorningstar1305

import React, { useState } from "react";

const FlashCard = ({ question }) => {
  const [isSelected, setSelected] = useState(false);

  const handleClick = () => {
    setSelected((s) => !s);
  };

  const handleMouseLeave = () => {

lucifermorningstar1305

import React from "react";
import FlashCard from "./FlashCard";

const FlashCards = ({ questions }) => {
  return (
    <div className="flashcards">
      {questions.map((question) => (
        <FlashCard question={question} key={question.id} />
      ))}
    </div>

lucifermorningstar1305

"""
This is an implementation of the Hill Climbing algorithm for the MountainCar-v1 (Continuous Spaces) environment. 
No gradient ascent is performed in this script.
"""

from typing import Callable, Dict, List, Any, Tuple, Optional

import numpy as np
import matplotlib.pyplot as plt
import torch

lucifermorningstar1305

yhat = MLJ.predict(tree, coerce(X[test, :], Continuous))

# To calculate the log-loss or binary cross entropy of our classification algorithm
println("Log-Loss on the Test Set : $(log_loss(yhat, coerce(y[test], Multiclass)) |> mean)")

# To calculate the accuracy of our classification algorithm
println("Accuracy on the Test Set : $(accuracy(mode.(yhat), coerce(y[test], Multiclass)))")

# To generate the Confusion Matrix of our Classification algorithm
ConfusionMatrix()(mode.(yhat), coerce(y[test], Multiclass))

lucifermorningstar1305

MLJ.fit!(tree, rows=train)

lucifermorningstar1305

rng = StableRNG(42)
train, test = partition(eachindex(y), 0.85, shuffle=true, rng=rng);

lucifermorningstar1305

tree = machine(tree_model, coerce(X, Continuous), coerce(y, Multiclass))

lucifermorningstar1305

DecisionTreeClassifier = @load DecisionTreeClassifier pkg=DecisionTree
tree_model = DecisionTreeClassifier()

lucifermorningstar1305

X, y = tfidf_mat, df[:, :Category]

lucifermorningstar1305

m = DocumentTermMatrix(crps)
tfidf_mat = tf_idf(m)